{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "22f22027",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-28T06:54:04.807849Z",
     "start_time": "2022-10-28T06:54:02.442993Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import metview as mv\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ipynb.fs.full.asm import seg2trans\n",
    "from ipynb.fs.full.asm import load_reg_nc\n",
    "# Executed in 1.79s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a20a4bd",
   "metadata": {},
   "source": [
    "## Load data: segments over the Malay Peninsula and Sumatra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8ef91cbc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-28T06:54:06.895825Z",
     "start_time": "2022-10-28T06:54:06.889191Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('/bog/amuttaqin/Datasets/Derived/segments_mc.csv')\n",
    "# Executed in 5ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2cdf9aed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-28T06:54:28.899605Z",
     "start_time": "2022-10-28T06:54:28.892390Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     mainland    lat1    lon1    lat2    lon2\n",
      "0  MalayPenin  14.900  97.800  13.600  98.300\n",
      "1  MalayPenin  11.611  98.784   8.923  98.324\n",
      "2  MalayPenin   7.700  99.200   6.700  99.900\n",
      "\n",
      "    mainland    lat1      lon1    lat2      lon2\n",
      "147   Isles2  0.2933  127.5815  0.7351  127.5314\n",
      "148   Isles2  1.0760  128.6925  1.5730  128.6859\n",
      "149   Isles2  1.7098  127.5678  1.2270  127.4403\n"
     ]
    }
   ],
   "source": [
    "df_MS = df\n",
    "#df_MS = df[(df['mainland'] == 'MalayPenin') | (df['mainland'] == 'Sumatra')]\n",
    "print(df_MS.head(n=3))\n",
    "print()\n",
    "print(df_MS.tail(n=3))\n",
    "# Executed in 10ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a889157",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-28T06:48:01.098258Z",
     "start_time": "2022-10-28T06:48:01.094615Z"
    }
   },
   "outputs": [],
   "source": [
    "print(df_MS[df_MS['mainland'] == 'MalayPenin'].shape)\n",
    "print(df_MS[df_MS['mainland'] == 'Sumatra'].shape)\n",
    "# Executed in 4ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f851b2d",
   "metadata": {},
   "source": [
    "<font color='blue'> Notes: There are 18 segments over the coast of Malay Peninsula, and 15 segments over Sumatra (18+15 = 33)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd930ba7",
   "metadata": {},
   "source": [
    "## Convert segments into transects for multivariable analyses "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ccb6d017",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-28T06:55:02.259436Z",
     "start_time": "2022-10-28T06:54:59.975637Z"
    }
   },
   "outputs": [],
   "source": [
    "transects04, transects_qc04 = seg2trans(seg=df_MS, dist2coast=0.4, dist2trnsc=0.3)\n",
    "# Executed in 503ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7d3e234",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-28T06:48:08.352837Z",
     "start_time": "2022-10-28T06:48:07.995502Z"
    }
   },
   "outputs": [],
   "source": [
    "transects02, transects_qc02 = seg2trans(seg=df_MS, dist2coast=0.2, dist2trnsc=0.3)\n",
    "# Executed in 368ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8b0af1d",
   "metadata": {},
   "source": [
    "<font color='blue'> Notes: Convert segments into transects with the following parameters: 1) distance to coast = 0.4 degree lat/lon for skin temperature difference analysis and 0.2 for wind breeze analysis, and 2) distance between transects = 0.3 degree lat/lon. These parameters can be adjusted for other type of analysis, e.g., wind breeze and boundary layer height analyses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd665af8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-28T06:55:04.873678Z",
     "start_time": "2022-10-28T06:55:04.868242Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(523, 7)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transects_qc04.shape\n",
    "# Executed in 5ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe8a62b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-28T06:48:13.081302Z",
     "start_time": "2022-10-28T06:48:13.078446Z"
    }
   },
   "outputs": [],
   "source": [
    "transects_qc02.shape\n",
    "# Executed in 3ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77fbb758",
   "metadata": {},
   "source": [
    "<font color='blue'> Notes: Total number of transects (after quality control process) for dist2coast = 0.4 is 153, and for dist2coast = 0.2 is 152."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "65d4dd5b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-28T06:55:08.247401Z",
     "start_time": "2022-10-28T06:55:08.237435Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>lat1</th>\n",
       "      <th>lon1</th>\n",
       "      <th>lat2</th>\n",
       "      <th>lon2</th>\n",
       "      <th>segment_index</th>\n",
       "      <th>lsm1</th>\n",
       "      <th>lsm2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.6864</td>\n",
       "      <td>97.8382</td>\n",
       "      <td>13.9736</td>\n",
       "      <td>98.5849</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.9664</td>\n",
       "      <td>97.7305</td>\n",
       "      <td>14.2536</td>\n",
       "      <td>98.4772</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.2464</td>\n",
       "      <td>97.6228</td>\n",
       "      <td>14.5336</td>\n",
       "      <td>98.3695</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lat1     lon1     lat2     lon2  segment_index  lsm1  lsm2\n",
       "0  13.6864  97.8382  13.9736  98.5849              1   0.0   1.0\n",
       "1  13.9664  97.7305  14.2536  98.4772              1   0.0   1.0\n",
       "2  14.2464  97.6228  14.5336  98.3695              1   0.0   1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transects_qc04.head(n=3)\n",
    "# Executed in 9ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3eac8517",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-28T06:48:16.279546Z",
     "start_time": "2022-10-28T06:48:16.271323Z"
    }
   },
   "outputs": [],
   "source": [
    "transects_qc02.head(n=3)\n",
    "# Executed in 10ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ed0f924",
   "metadata": {},
   "source": [
    "## Display the segments and transects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8babf767",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-28T06:55:13.605016Z",
     "start_time": "2022-10-28T06:55:13.600974Z"
    }
   },
   "outputs": [],
   "source": [
    "region_segments = df_MS[[\"lat1\",\"lon1\",\"lat2\",\"lon2\"]].to_numpy().tolist()\n",
    "region_transects = transects_qc04[[\"lat1\",\"lon1\",\"lat2\",\"lon2\"]].to_numpy().tolist()\n",
    "# # Executed in 4ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8114ee1",
   "metadata": {},
   "source": [
    "<font color='blue'> Notes: To display the transects, select between transects_qc04 and transects_qc02."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b54c331b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-28T06:55:44.579524Z",
     "start_time": "2022-10-28T06:55:16.812576Z"
    }
   },
   "outputs": [],
   "source": [
    "geolines_segments = []\n",
    "for i in range(len(region_segments)):\n",
    "    lnss = mv.mvl_geoline(*region_segments[i],1)\n",
    "    geolines_segments.append(lnss)\n",
    "\n",
    "geolines_transects = []\n",
    "for j in range(len(region_transects)):\n",
    "    lnst = mv.mvl_geoline(*region_transects[j],1)\n",
    "    geolines_transects.append(lnst)\n",
    "# Executed in 7.84s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e3cefb3b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-28T06:56:35.597153Z",
     "start_time": "2022-10-28T06:56:27.499132Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mv.setoutput(\"jupyter\", plot_widget=False, output_width=900)\n",
    "#mv.setoutput(mv.pdf_output(output_name=\"/bog/amuttaqin/Figures/\"+reg+\"-trans-segs\"))\n",
    "my_view = mv.geoview(\n",
    "    map_area_definition=\"corners\", \n",
    "    area=[-15.1,89.9,15.1,160.1],\n",
    "    subpage_y_length=80,\n",
    "    subpage_y_position=10, \n",
    "    subpage_x_position=21,\n",
    "    subpage_frame_colour=\"black\",\n",
    "    subpage_frame_thickness=10)\n",
    "my_coast = mv.mcoast(\n",
    "    map_coastline_resolution = \"high\",\n",
    "    map_coastline_thickness = 2,\n",
    "    map_coastline_sea_shade = \"on\",\n",
    "    map_coastline_sea_shade_colour = \"RGB(0.4845,0.6572,0.9351)\",\n",
    "    map_coastline_land_shade = \"on\",\n",
    "    map_coastline_land_shade_colour = \"beige\",\n",
    "    map_cities = \"on\",\n",
    "    map_cities_font_size = 3,\n",
    "    map_cities_marker = \"circle\",\n",
    "    map_cities_marker_colour = \"black\",\n",
    "    map_cities_marker_height = 2,\n",
    "    map_grid_line_style = \"dash\",\n",
    "    map_grid_latitude_increment = 4, #5 \n",
    "    map_grid_longitude_increment = 4, #5\n",
    "    map_label =\"on\",\n",
    "    map_label_height = 0.7,\n",
    "    map_label_top = \"off\",\n",
    "    map_label_latitude_frequency=1,\n",
    "    map_label_longitude_frequency=1)\n",
    "pltLst = []\n",
    "graph_segs = mv.mgraph(\n",
    "    graph_line_colour  = \"red\",\n",
    "    graph_line_thickness = 8.0,\n",
    "    graph_line_style = \"solid\")\n",
    "pltLst.extend([geolines_segments, graph_segs])\n",
    "graph_trans = mv.mgraph(\n",
    "   graph_line_colour  = \"blue\",\n",
    "   graph_line_thickness = 5.0,\n",
    "   graph_line_style = \"solid\")\n",
    "pltLst.extend([geolines_transects, graph_trans])\n",
    "mv.plot(my_view, my_coast, pltLst)\n",
    "# Executed in 1.78s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "045a4753",
   "metadata": {},
   "source": [
    "<font color='blue'> The analyses is focused on the Malay Peninsula and Sumatra region as shown in the figure above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a69f6805",
   "metadata": {},
   "source": [
    "## Load reanalysis datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9852651a",
   "metadata": {},
   "source": [
    "<font color='blue'> Load skin temperature over ocean and land:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a9c21fb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:01:12.951104Z",
     "start_time": "2022-07-10T02:01:12.111264Z"
    }
   },
   "outputs": [],
   "source": [
    "filein1 = \"/bog/amuttaqin/Datasets/ERA5/skto/1991-2020_skto_\"\n",
    "filein2 = \"/bog/amuttaqin/Datasets/ERA5-Land/skt/1991-2020_skt_\"\n",
    "\n",
    "skto_MS = load_reg_nc(filein1, 'MalayPenin_Sumatra')\n",
    "sktl_MS = load_reg_nc(filein2, 'MalayPenin_Sumatra')\n",
    "# Executed in 512ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b7974f6",
   "metadata": {},
   "source": [
    "<font color='blue'> Load vertically-averaged and two-level low-level wind:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0faf2ebc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:01:16.875472Z",
     "start_time": "2022-07-10T02:01:16.296157Z"
    }
   },
   "outputs": [],
   "source": [
    "filein3 = \"/bog/amuttaqin/Datasets/ERA5/uv/uv_low_mean/1991-2020_uv_low_mean_\"\n",
    "filein4 = \"/bog/amuttaqin/Datasets/ERA5/uv/uv_low/1991-2020_uv_low_\"\n",
    "filein5 = \"/bog/amuttaqin/Datasets/ERA5/uv/uv_multilevel/1991-2020_uv_multilevel_\"\n",
    "\n",
    "windlm_MS = load_reg_nc(filein3, 'MalayPenin_Sumatra')\n",
    "windlo_MS = load_reg_nc(filein4, 'MalayPenin_Sumatra')\n",
    "windml_MS = load_reg_nc(filein5, 'MalayPenin_Sumatra')\n",
    "# Executed in 1.11s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c94bf6b4",
   "metadata": {},
   "source": [
    "<font color='blue'> Load planetary boundary layer height:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a64fe65",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:01:20.113982Z",
     "start_time": "2022-07-10T02:01:19.914763Z"
    }
   },
   "outputs": [],
   "source": [
    "filein6 = \"/bog/amuttaqin/Datasets/ERA5/pbl_height/1991-2020_blh_\"\n",
    "pblh_MS = load_reg_nc(filein6, 'MalayPenin_Sumatra')\n",
    "# Executed in 216ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "957ab851",
   "metadata": {},
   "source": [
    "<font color='blue'> Load top-layer (0-7 cm) soil moisture:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8180aa9d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:01:25.759193Z",
     "start_time": "2022-07-10T02:01:25.542542Z"
    }
   },
   "outputs": [],
   "source": [
    "filein7 = \"/bog/amuttaqin/Datasets/ERA5-Land/swvl1/1991-2020_swvl1_\"\n",
    "swvl1_MS = load_reg_nc(filein7, 'MalayPenin_Sumatra')\n",
    "# Executed in 188ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbf43406",
   "metadata": {},
   "source": [
    "<font color='blue'> Convert regular dataset to chunked/dask dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1764398e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:01:28.165104Z",
     "start_time": "2022-07-10T02:01:28.153376Z"
    }
   },
   "outputs": [],
   "source": [
    "skto_MS = skto_MS.chunk(chunks={\"time\":24*365*2})\n",
    "sktl_MS = sktl_MS.chunk(chunks={\"time\":24*365*2})\n",
    "windlm_MS = windlm_MS.chunk(chunks={\"time\":24*365*2})\n",
    "windlo_MS = windlo_MS.chunk(chunks={\"time\":24*365*2})\n",
    "windml_MS = windml_MS.chunk(chunks={\"time\":24*365*2})\n",
    "pblh_MS = pblh_MS.chunk(chunks={\"time\":24*365*2})\n",
    "swvl1_MS = swvl1_MS.chunk(chunks={\"time\":24*365*2})\n",
    "# Executed in 9ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5375fedf",
   "metadata": {},
   "source": [
    "<font color='blue'> Example to show chunked xarray.DataArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51b10c78",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:01:30.889543Z",
     "start_time": "2022-07-10T02:01:30.768182Z"
    }
   },
   "outputs": [],
   "source": [
    "skto_MS.var235"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08ade988",
   "metadata": {},
   "source": [
    "## Extract reanalysis aligned with transects"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6444a26",
   "metadata": {},
   "source": [
    "### Skin temperature difference: land-ocean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a4cb4ae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:04:47.905562Z",
     "start_time": "2022-07-10T02:01:43.826812Z"
    }
   },
   "outputs": [],
   "source": [
    "lat1 = transects_qc04[\"lat1\"].to_numpy().tolist()\n",
    "lon1 = transects_qc04[\"lon1\"].to_numpy().tolist()\n",
    "lat2 = transects_qc04[\"lat2\"].to_numpy().tolist()\n",
    "lon2 = transects_qc04[\"lon2\"].to_numpy().tolist()\n",
    "\n",
    "lat1 = xr.DataArray(lat1, dims='segment')\n",
    "lon1 = xr.DataArray(lon1, dims='segment')\n",
    "lat2 = xr.DataArray(lat2, dims='segment')\n",
    "lon2 = xr.DataArray(lon2, dims='segment')\n",
    "\n",
    "ts1 = skto_MS.sel(lat=lat1, lon=lon1, method=\"nearest\")\n",
    "ts2 = sktl_MS.sel(lat=lat2, lon=lon2, method=\"nearest\")\n",
    "\n",
    "ts1 = ts1.compute()\n",
    "ts2 = ts2.compute()\n",
    "\n",
    "tdiff = ts2 - ts1\n",
    "tdiff = tdiff.compute()\n",
    "# Executed in 3m 23s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2bdd186",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:04:58.046658Z",
     "start_time": "2022-07-10T02:04:58.043585Z"
    }
   },
   "outputs": [],
   "source": [
    "tdiff = tdiff.assign_coords(segment=transects_qc04[\"segment_index\"].to_numpy().tolist())\n",
    "# Executed in 3ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60250d24",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:05:38.796883Z",
     "start_time": "2022-07-10T02:05:38.794257Z"
    }
   },
   "outputs": [],
   "source": [
    "tdiff = tdiff.rename({'var235':'skt'})\n",
    "# Executed in 3ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e23b02d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:08:38.954915Z",
     "start_time": "2022-07-10T02:08:38.952619Z"
    }
   },
   "outputs": [],
   "source": [
    "tdiff.attrs['global attr'] = 'Skin temperature difference between land and ocean at each designated transects in degree Celcius.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8304dee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:09:39.528564Z",
     "start_time": "2022-07-10T02:09:39.516992Z"
    }
   },
   "outputs": [],
   "source": [
    "tdiff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e39203f7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:09:44.907689Z",
     "start_time": "2022-07-10T02:09:44.688687Z"
    }
   },
   "outputs": [],
   "source": [
    "tdiff.to_netcdf(path=\"/bog/amuttaqin/Datasets/Derived/tdiff_MalayPenin_Sumatra.nc\")\n",
    "#tdiff = xr.open_dataset('/bog/amuttaqin/Datasets/Derived/tdiff_MalayPenin_Sumatra.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b483c9f6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:12:19.460897Z",
     "start_time": "2022-07-10T02:12:17.989551Z"
    }
   },
   "outputs": [],
   "source": [
    "data = tdiff.skt.groupby('time.hour').mean(dim='time').\\\n",
    "       groupby('segment').mean(dim='segment')\n",
    "\n",
    "fig1, (ax0, ax1) = plt.subplots(nrows=2, ncols=1, dpi=75, figsize=(6,8))\n",
    "im = ax0.pcolormesh(data.T, cmap='RdBu_r', vmin=-10, vmax=10)\n",
    "cbar = fig1.colorbar(im, ax=ax0)\n",
    "cbar.set_label('$\\Delta$T ($^\\circ$C)', fontsize=16)\n",
    "cbar.ax.tick_params(labelsize=12)\n",
    "ax0.set_xlabel('hour (UTC)', fontsize=16)\n",
    "ax0.set_ylabel('Segment', fontsize=16)\n",
    "ax0.tick_params(axis='both', labelsize=12)\n",
    "\n",
    "ax1.plot(data)\n",
    "ax1.set_xlabel('hour (UTC)', fontsize=16)\n",
    "ax1.set_ylabel('$\\Delta$T ($^\\circ$C)', fontsize=16)\n",
    "ax1.tick_params(axis='both', labelsize=12)\n",
    "\n",
    "fig1.tight_layout()\n",
    "fig1.savefig(\"/bog/amuttaqin/Figures/tdiff_MalayPenin_Sumatra.png\")\n",
    "# Executed in 592ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6168f74d",
   "metadata": {},
   "source": [
    "### Wind breeze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a56f9739",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:18:04.557945Z",
     "start_time": "2022-07-10T02:12:36.651306Z"
    }
   },
   "outputs": [],
   "source": [
    "lat2 = transects_qc02[\"lat2\"].to_numpy().tolist()\n",
    "lon2 = transects_qc02[\"lon2\"].to_numpy().tolist()\n",
    "\n",
    "lat2 = xr.DataArray(lat2, dims='segment')\n",
    "lon2 = xr.DataArray(lon2, dims='segment')\n",
    "\n",
    "tsw1 = windlm_MS.sel(lat=lat2, lon=lon2, method=\"nearest\")   # vertically-averaged low mean \n",
    "tsw2 = windlo_MS.sel(lat=lat2, lon=lon2, method=\"nearest\")   # two-level low mean\n",
    "\n",
    "tsw1 = tsw1.compute()\n",
    "tsw2 = tsw2.compute()\n",
    "# Executed in 5m 29s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a6713a2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:20:49.520155Z",
     "start_time": "2022-07-10T02:20:49.515381Z"
    }
   },
   "outputs": [],
   "source": [
    "tsw1 = tsw1.assign_coords(segment=transects_qc02[\"segment_index\"].to_numpy().tolist())\n",
    "tsw2 = tsw2.assign_coords(segment=transects_qc02[\"segment_index\"].to_numpy().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a38047b0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:20:52.649191Z",
     "start_time": "2022-07-10T02:20:52.646158Z"
    }
   },
   "outputs": [],
   "source": [
    "tsw1 = tsw1.rename({'var131':'u', 'var132':'v'})\n",
    "tsw2 = tsw2.rename({'var131':'u', 'var132':'v'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be00300a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:20:55.142244Z",
     "start_time": "2022-07-10T02:20:55.139582Z"
    }
   },
   "outputs": [],
   "source": [
    "tsw1 = tsw1.drop_vars(\"lon\")\n",
    "tsw1 = tsw1.drop_vars(\"lat\")\n",
    "tsw2 = tsw2.drop_vars(\"lon\")\n",
    "tsw2 = tsw2.drop_vars(\"lat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f524b801",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:00.284413Z",
     "start_time": "2022-07-08T16:56:00.282966Z"
    }
   },
   "outputs": [],
   "source": [
    "#tsw1.to_netcdf(path=\"/bog/amuttaqin/Datasets/Derived/tsw1_MalayPenin_Sumatra.nc\")\n",
    "#tsw2.to_netcdf(path=\"/bog/amuttaqin/Datasets/Derived/tsw2_MalayPenin_Sumatra.nc\")\n",
    "\n",
    "#tsw1 = xr.open_dataset(\"/bog/amuttaqin/Datasets/Derived/tsw1_MalayPenin_Sumatra.nc\")\n",
    "#tsw2 = xr.open_dataset(\"/bog/amuttaqin/Datasets/Derived/tsw2_MalayPenin_Sumatra.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6916e92",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:21:06.392684Z",
     "start_time": "2022-07-10T02:21:06.378519Z"
    }
   },
   "outputs": [],
   "source": [
    "tsw1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ba008ed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:21:10.056250Z",
     "start_time": "2022-07-10T02:21:10.040196Z"
    }
   },
   "outputs": [],
   "source": [
    "tsw2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e0dd131",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:21:16.667639Z",
     "start_time": "2022-07-10T02:21:14.990486Z"
    }
   },
   "outputs": [],
   "source": [
    "windslm = np.sqrt(tsw1.u**2+tsw1.v**2)\n",
    "windslo = np.sqrt(tsw2.u**2+tsw2.v**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6fc82ae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:21:27.301592Z",
     "start_time": "2022-07-10T02:21:27.298949Z"
    }
   },
   "outputs": [],
   "source": [
    "windslm = windslm.to_dataset(name='spd')\n",
    "windslo = windslo.to_dataset(name='spd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ad9f8c2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:23:42.951505Z",
     "start_time": "2022-07-10T02:23:42.949326Z"
    }
   },
   "outputs": [],
   "source": [
    "windslm.attrs['global attr'] = 'Low-level (1000-950 hPa) vertically-averaged horizontal wind speed'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c46bd1a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:24:21.178159Z",
     "start_time": "2022-07-10T02:24:21.087064Z"
    }
   },
   "outputs": [],
   "source": [
    "windslm.to_netcdf('/bog/amuttaqin/Datasets/Derived/windslm_MalayPenin_Sumatra.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5777eae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:25:32.766499Z",
     "start_time": "2022-07-10T02:25:32.764270Z"
    }
   },
   "outputs": [],
   "source": [
    "windslo.attrs['global attr'] = 'Low-level (1000-950 hPa) horizontal wind speed'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e118dbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:26:14.191846Z",
     "start_time": "2022-07-10T02:26:13.979571Z"
    }
   },
   "outputs": [],
   "source": [
    "windslo.to_netcdf('/bog/amuttaqin/Datasets/Derived/windslo_MalayPenin_Sumatra.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d38f1dc7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:21:41.998840Z",
     "start_time": "2022-07-10T02:21:41.354994Z"
    }
   },
   "outputs": [],
   "source": [
    "data2 = windslm.spd.groupby('time.hour').mean(dim='time').\\\n",
    "       groupby('segment').mean(dim='segment')\n",
    "\n",
    "fig2, (ax0, ax1) = plt.subplots(nrows=2, ncols=1, dpi=75, figsize=(6,8))\n",
    "im = ax0.pcolormesh(data2.T)\n",
    "cbar = fig2.colorbar(im, ax=ax0)\n",
    "cbar.set_label('U (m/s)', fontsize=16)\n",
    "cbar.ax.tick_params(labelsize=12)\n",
    "ax0.set_xlabel('hour (UTC)', fontsize=16)\n",
    "ax0.set_ylabel('Segment', fontsize=16)\n",
    "ax0.tick_params(axis='both', labelsize=12)\n",
    "\n",
    "ax1.plot(data2)\n",
    "ax1.set_xlabel('hour (UTC)', fontsize=16)\n",
    "ax1.set_ylabel('U (m/s)', fontsize=16)\n",
    "ax1.tick_params(axis='both', labelsize=12)\n",
    "\n",
    "fig2.tight_layout()\n",
    "fig2.savefig(\"/bog/amuttaqin/Figures/windslm_MalayPenin_Sumatra.png\")\n",
    "# Executed in 592ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc1021ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:21:48.315374Z",
     "start_time": "2022-07-10T02:21:47.074264Z"
    }
   },
   "outputs": [],
   "source": [
    "data3 = windslo.spd.sel(plev=100000).groupby('time.hour').mean(dim='time').\\\n",
    "       groupby('segment').mean(dim='segment')\n",
    "\n",
    "data4 = windslo.spd.sel(plev=95000).groupby('time.hour').mean(dim='time').\\\n",
    "       groupby('segment').mean(dim='segment')\n",
    "\n",
    "fig3, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2, dpi=75, figsize=(12,8))\n",
    "\n",
    "im1 = ax0.pcolormesh(data3.T)\n",
    "cbar1 = fig3.colorbar(im1, ax=ax0)\n",
    "cbar1.set_label('$U_{1000}$ (m/s)', fontsize=16)\n",
    "cbar1.ax.tick_params(labelsize=12)\n",
    "ax0.set_xlabel('hour (UTC)', fontsize=16)\n",
    "ax0.set_ylabel('Segment', fontsize=16)\n",
    "ax0.tick_params(axis='both', labelsize=12)\n",
    "\n",
    "im2 = ax1.pcolormesh(data4.T)\n",
    "cbar2 = fig3.colorbar(im2, ax=ax1)\n",
    "cbar2.set_label('$U_{950}$ (m/s)', fontsize=16)\n",
    "cbar2.ax.tick_params(labelsize=12)\n",
    "ax1.set_xlabel('hour (UTC)', fontsize=16)\n",
    "ax1.tick_params(axis='both', labelsize=12)\n",
    "\n",
    "ax2.plot(data3)\n",
    "ax2.set_xlabel('hour (UTC)', fontsize=16)\n",
    "ax2.set_ylabel('$U_{1000}$ (m/s)', fontsize=16)\n",
    "ax2.tick_params(axis='both', labelsize=12)\n",
    "\n",
    "ax3.plot(data4)\n",
    "ax3.set_xlabel('hour (UTC)', fontsize=16)\n",
    "ax3.set_ylabel('$U_{950}$ (m/s)', fontsize=16)\n",
    "ax3.tick_params(axis='both', labelsize=12)\n",
    "\n",
    "fig3.tight_layout()\n",
    "fig3.savefig(\"/bog/amuttaqin/Figures/windslo1000_MalayPenin_Sumatra.png\")\n",
    "# Executed in 592ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a498df6",
   "metadata": {},
   "source": [
    "### Boundary layer height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "344f40e6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:29:05.825120Z",
     "start_time": "2022-07-10T02:28:22.091282Z"
    }
   },
   "outputs": [],
   "source": [
    "lat2 = transects_qc02[\"lat2\"].to_numpy().tolist()\n",
    "lon2 = transects_qc02[\"lon2\"].to_numpy().tolist()\n",
    "\n",
    "lat2 = xr.DataArray(lat2, dims='segment')\n",
    "lon2 = xr.DataArray(lon2, dims='segment')\n",
    "\n",
    "pblh = pblh_MS.sel(lat=lat2, lon=lon2, method=\"nearest\")   \n",
    "pblh = pblh.compute()\n",
    "# Executed in 5m 29s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "181d58fb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:29:09.159121Z",
     "start_time": "2022-07-10T02:29:09.155854Z"
    }
   },
   "outputs": [],
   "source": [
    "pblh = pblh.assign_coords(segment=transects_qc02[\"segment_index\"].to_numpy().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bbeab80",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:29:12.208575Z",
     "start_time": "2022-07-10T02:29:12.206011Z"
    }
   },
   "outputs": [],
   "source": [
    "pblh = pblh.rename({'var159': 'blh'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee4d1822",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:29:14.534653Z",
     "start_time": "2022-07-10T02:29:14.532446Z"
    }
   },
   "outputs": [],
   "source": [
    "pblh = pblh.drop_vars(\"lon\")\n",
    "pblh = pblh.drop_vars(\"lat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dc2b76f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:30:35.765020Z",
     "start_time": "2022-07-10T02:30:35.752031Z"
    }
   },
   "outputs": [],
   "source": [
    "pblh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36fb889b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T02:35:09.371427Z",
     "start_time": "2022-07-10T02:34:57.090357Z"
    }
   },
   "outputs": [],
   "source": [
    "pblh.to_netcdf(path=\"/bog/amuttaqin/Datasets/Derived/pblh_MalayPenin_Sumatra.nc\")\n",
    "#pblh = xr.open_dataset(\"/bog/amuttaqin/Datasets/Derived/pblh_MalayPenin_Sumatra.nc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fada411",
   "metadata": {},
   "source": [
    "### What we have so far?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a54934cf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:50.145582Z",
     "start_time": "2022-07-08T16:56:50.135921Z"
    }
   },
   "outputs": [],
   "source": [
    "tdiff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85463c92",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:50.157663Z",
     "start_time": "2022-07-08T16:56:50.146682Z"
    }
   },
   "outputs": [],
   "source": [
    "windslm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d2a70ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:50.171621Z",
     "start_time": "2022-07-08T16:56:50.158715Z"
    }
   },
   "outputs": [],
   "source": [
    "windslo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c522311",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:50.183814Z",
     "start_time": "2022-07-08T16:56:50.172699Z"
    }
   },
   "outputs": [],
   "source": [
    "pblh"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e46790bc",
   "metadata": {},
   "source": [
    "## Removing seasonality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "535b34f1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:52.185555Z",
     "start_time": "2022-07-08T16:56:50.184896Z"
    }
   },
   "outputs": [],
   "source": [
    "tdiff_clim = tdiff.groupby('time.hour').mean('time')\n",
    "tdiff_anom = tdiff.groupby('time.hour') - tdiff_clim\n",
    "#tdiff_anom = tdiff_anom.drop_vars('hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bcc6f98",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:52.200316Z",
     "start_time": "2022-07-08T16:56:52.186983Z"
    }
   },
   "outputs": [],
   "source": [
    "tdiff_anom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4eddfa38",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:53.331639Z",
     "start_time": "2022-07-08T16:56:52.201443Z"
    }
   },
   "outputs": [],
   "source": [
    "windslm_clim = windslm.groupby('time.hour').mean('time')\n",
    "windslm_anom = windslm.groupby('time.hour') - windslm_clim\n",
    "#windslm_anom = windslm_anom.drop_vars('hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6244b40",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:53.346123Z",
     "start_time": "2022-07-08T16:56:53.333037Z"
    }
   },
   "outputs": [],
   "source": [
    "windslm_anom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e268f14e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:55.266527Z",
     "start_time": "2022-07-08T16:56:53.347239Z"
    }
   },
   "outputs": [],
   "source": [
    "windslo_clim = windslo.groupby('time.hour').mean('time')\n",
    "windslo_anom = windslo.groupby('time.hour') - windslo_clim\n",
    "#windslo_anom = windslo_anom.drop_vars('hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "249f7186",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:55.283104Z",
     "start_time": "2022-07-08T16:56:55.267925Z"
    }
   },
   "outputs": [],
   "source": [
    "windslo_anom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e0ef917",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:56.148125Z",
     "start_time": "2022-07-08T16:56:55.284264Z"
    }
   },
   "outputs": [],
   "source": [
    "pblh_clim = pblh.groupby('time.hour').mean('time')\n",
    "pblh_anom = pblh.groupby('time.hour') - pblh_clim\n",
    "#pblh_anom = pblh_anom.drop_vars('hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b25a05e2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:56.162563Z",
     "start_time": "2022-07-08T16:56:56.149515Z"
    }
   },
   "outputs": [],
   "source": [
    "pblh_anom"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37a68fcf",
   "metadata": {},
   "source": [
    "## What we have so far?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff31fa06",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:56.208345Z",
     "start_time": "2022-07-08T16:56:56.163713Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#tdiff_anom.to_netcdf('/bog/amuttaqin/Datasets/Derived/tdiff_anom_MalayPenin_Sumatra.nc')\n",
    "tdiff_anom = xr.open_dataset('/bog/amuttaqin/Datasets/Derived/tdiff_anom_MalayPenin_Sumatra.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c9ebeab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:56.220123Z",
     "start_time": "2022-07-08T16:56:56.209542Z"
    }
   },
   "outputs": [],
   "source": [
    "#windslm_anom.to_netcdf('/bog/amuttaqin/Datasets/Derived/windslm_anom_MalayPenin_Sumatra.nc')\n",
    "windslm_anom = xr.open_dataset('/bog/amuttaqin/Datasets/Derived/windslm_anom_MalayPenin_Sumatra.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ef01906",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:56.258594Z",
     "start_time": "2022-07-08T16:56:56.221385Z"
    }
   },
   "outputs": [],
   "source": [
    "#windslo_anom.to_netcdf('/bog/amuttaqin/Datasets/Derived/windslo_anom_MalayPenin_Sumatra.nc')\n",
    "windslo_anom = xr.open_dataset('/bog/amuttaqin/Datasets/Derived/windslo_anom_MalayPenin_Sumatra.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4281868",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:56.297426Z",
     "start_time": "2022-07-08T16:56:56.259829Z"
    }
   },
   "outputs": [],
   "source": [
    "#pblh_anom.to_netcdf('/bog/amuttaqin/Datasets/Derived/pblh_anom_MalayPenin_Sumatra.nc')\n",
    "pblh_anom = xr.open_dataset('/bog/amuttaqin/Datasets/Derived/pblh_anom_MalayPenin_Sumatra.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1aa0f5d6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:56.300371Z",
     "start_time": "2022-07-08T16:56:56.298647Z"
    }
   },
   "outputs": [],
   "source": [
    "#tdiff.skt.groupby('time.hour').mean('time').groupby('segment').mean('segment').plot(x='hour')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d148c33",
   "metadata": {},
   "source": [
    "## Scatter plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "295f3d23",
   "metadata": {},
   "source": [
    "<font color='blue'> Segments for further exploration: 5, 6, 12, 15, 16, 18, 26, 29"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e05ba5fb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:56.303271Z",
     "start_time": "2022-07-08T16:56:56.301469Z"
    }
   },
   "outputs": [],
   "source": [
    "seg=12\n",
    "year1='1991'\n",
    "year2='2020'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54be9d39",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:58.472143Z",
     "start_time": "2022-07-08T16:56:56.304423Z"
    }
   },
   "outputs": [],
   "source": [
    "fig4, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2, figsize=(10,8))\n",
    "\n",
    "ax0.scatter(x=tdiff_anom.skt.sel(time=slice(year1,year2)).sel(segment=seg).mean(dim='segment'), \n",
    "            y=windslm_anom.spd.sel(time=slice(year1,year2)).sel(segment=seg).mean(dim='segment'))\n",
    "ax0.set_xlabel(\"$\\Delta$T (C)\", fontsize=14)\n",
    "ax0.set_ylabel(\"$U_{1000-950}$ (m/s)\", fontsize=14)\n",
    "ax0.set_ylim(-8,16)\n",
    "\n",
    "ax1.scatter(x=tdiff_anom.skt.sel(time=slice(year1,year2)).sel(segment=seg).mean(dim='segment'), \n",
    "            y=windslo_anom.spd.sel(time=slice(year1,year2)).sel(plev=100000).sel(segment=seg).mean(dim='segment'))\n",
    "ax1.set_xlabel(\"$\\Delta$T (C)\", fontsize=14)\n",
    "ax1.set_ylabel(\"$U_{1000}$ (m/s)\", fontsize=14)\n",
    "ax1.set_ylim(-8,16)\n",
    "\n",
    "ax2.scatter(x=tdiff_anom.skt.sel(time=slice(year1,year2)).sel(segment=seg).mean(dim='segment'), \n",
    "            y=windslo_anom.spd.sel(time=slice(year1,year2)).sel(plev=95000).sel(segment=seg).mean(dim='segment'))\n",
    "ax2.set_xlabel(\"$\\Delta$T (C)\", fontsize=14)\n",
    "ax2.set_ylabel(\"$U_{950}$ (m/s)\", fontsize=14)\n",
    "ax2.set_ylim(-8,16)\n",
    "\n",
    "ax3.scatter(x=tdiff_anom.skt.sel(time=slice(year1,year2)).sel(segment=seg).mean(dim='segment'), \n",
    "            y=pblh_anom.blh.sel(time=slice(year1,year2)).sel(segment=seg).mean(dim='segment'))\n",
    "ax3.set_xlabel(\"$\\Delta$T (C)\", fontsize=14)\n",
    "ax3.set_ylabel(\"BLH (m)\", fontsize=14)\n",
    "\n",
    "fig4.tight_layout()\n",
    "#fig4.savefig(\"scatter_plot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98faf00f",
   "metadata": {},
   "source": [
    "## Turbulence Kinetic Energy Approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69f75a08",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:59.565435Z",
     "start_time": "2022-07-08T16:56:58.473484Z"
    }
   },
   "outputs": [],
   "source": [
    "U = windslm_anom.spd\n",
    "z = pblh_anom.blh\n",
    "tke = (U**2)*z\n",
    "udz = U/z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96fadf8f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:59.569335Z",
     "start_time": "2022-07-08T16:56:59.567090Z"
    }
   },
   "outputs": [],
   "source": [
    "tke = tke.to_dataset(name='tke')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "caea59a6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:59.585009Z",
     "start_time": "2022-07-08T16:56:59.570579Z"
    }
   },
   "outputs": [],
   "source": [
    "tke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f1eab60",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:56:59.587771Z",
     "start_time": "2022-07-08T16:56:59.586228Z"
    }
   },
   "outputs": [],
   "source": [
    "#udz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec70f48b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:57:00.581936Z",
     "start_time": "2022-07-08T16:56:59.588969Z"
    }
   },
   "outputs": [],
   "source": [
    "fig5, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(12,6))\n",
    "\n",
    "ax0.scatter(x=tdiff_anom.skt.sel(time=slice(year1,year2)).sel(segment=seg).mean(dim='segment'), \n",
    "            y=tke.tke.sel(time=slice(year1,year2)).sel(segment=seg).mean(dim='segment'))\n",
    "ax0.set_xlabel(\"$\\Delta$T (C)\", fontsize=14)\n",
    "ax0.set_ylabel(\"$U^{2}z$ $(m^{3} s^{-2})$\", fontsize=14)\n",
    "\n",
    "ax1.scatter(x=tdiff_anom.skt.sel(time=slice(year1,year2)).sel(segment=seg).mean(dim='segment'), \n",
    "            y=udz.sel(time=slice(year1,year2)).sel(segment=seg).mean(dim='segment'))\n",
    "ax1.set_xlabel(\"$\\Delta$T (C)\", fontsize=14)\n",
    "ax1.set_ylabel(\"U/z ($s^{-1}$)\", fontsize=14)\n",
    "\n",
    "fig5.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e333d918",
   "metadata": {},
   "source": [
    "## Binned scatter plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a01279ac",
   "metadata": {},
   "source": [
    "delta T vs windslm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdab1893",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:57:03.605904Z",
     "start_time": "2022-07-08T16:57:00.583244Z"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1d63c27",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:57:03.610070Z",
     "start_time": "2022-07-08T16:57:03.607331Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_theme(style='darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92af7ddd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:57:03.767834Z",
     "start_time": "2022-07-08T16:57:03.611377Z"
    }
   },
   "outputs": [],
   "source": [
    "ts = xr.merge([tdiff_anom.skt.sel(time=slice('1991','1995')).sel(segment=seg).mean(dim='segment'), \n",
    "               windslm_anom.spd.sel(time=slice('1991','1995')).sel(segment=seg).mean(dim='segment'), \n",
    "               pblh_anom.blh.sel(time=slice('1991','1995')).sel(segment=seg).mean(dim='segment'),\n",
    "               tke.sel(time=slice('1991','1995')).sel(segment=seg).mean(dim='segment')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa8f6934",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:57:03.784721Z",
     "start_time": "2022-07-08T16:57:03.769397Z"
    }
   },
   "outputs": [],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c453f929",
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2022-07-08T16:57:03.785943Z"
    }
   },
   "outputs": [],
   "source": [
    "df2 = ts.to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1664893b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:57:03.799909Z",
     "start_time": "2022-07-08T16:57:03.789872Z"
    }
   },
   "outputs": [],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f0e9f75",
   "metadata": {
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     "start_time": "2022-07-08T16:57:03.801032Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.scatterplot(x='skt', y='spd', data=df2)\n",
    "plt.title(\"Wind breeze by skin temperature difference\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4113c260",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:57:07.258487Z",
     "start_time": "2022-07-08T16:57:04.009287Z"
    }
   },
   "outputs": [],
   "source": [
    "import binsreg\n",
    "\n",
    "def binscatter(**kwargs):\n",
    "    # Estimate binsreg\n",
    "    est = binsreg.binsreg(**kwargs)\n",
    "    \n",
    "    # Retrieve estimates\n",
    "    df_est = pd.concat([d.dots for d in est.data_plot])\n",
    "    df_est = df_est.rename(columns={'x': kwargs.get(\"x\"), 'fit': kwargs.get(\"y\")})\n",
    "    \n",
    "    # Add confidence intervals\n",
    "    if \"ci\" in kwargs:\n",
    "        df_est = pd.merge(df_est, pd.concat([d.ci for d in est.data_plot]))\n",
    "        df_est = df_est.drop(columns=['x'])\n",
    "        df_est['ci'] = df_est['ci_r'] - df_est['ci_l']\n",
    "    \n",
    "    # Rename groups\n",
    "    if \"by\" in kwargs:\n",
    "        df_est['group'] = df_est['group'].astype(df[kwargs.get(\"by\")].dtype)\n",
    "        df_est = df_est.rename(columns={'group': kwargs.get(\"by\")})\n",
    "\n",
    "    return df_est"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d3c12c7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:57:36.806738Z",
     "start_time": "2022-07-08T16:57:07.259863Z"
    }
   },
   "outputs": [],
   "source": [
    "# Estimate binsreg\n",
    "df_est = binscatter(x='skt', y='spd', data=df2, ci=(3,3))\n",
    "df_est.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77867a41",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:57:36.984345Z",
     "start_time": "2022-07-08T16:57:36.809482Z"
    }
   },
   "outputs": [],
   "source": [
    "# Plot binned scatterplot\n",
    "sns.scatterplot(x='skt', y='spd', data=df_est);\n",
    "plt.errorbar('skt', 'spd', yerr='ci', data=df_est, ls='', lw=2, alpha=0.2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aab96b4c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:57:54.156750Z",
     "start_time": "2022-07-08T16:57:36.986049Z"
    }
   },
   "outputs": [],
   "source": [
    "# Estimate binsreg\n",
    "df_est2 = binscatter(x='skt', y='tke', data=df2, ci=(3,3))\n",
    "df_est2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "088a666c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T16:57:54.331974Z",
     "start_time": "2022-07-08T16:57:54.159234Z"
    }
   },
   "outputs": [],
   "source": [
    "# Plot binned scatterplot\n",
    "sns.scatterplot(x='skt', y='tke', data=df_est2);\n",
    "plt.errorbar('skt', 'tke', yerr='ci', data=df_est2, ls='', lw=2, alpha=0.2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3da615a2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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