{
"cells": [
{
"cell_type": "markdown",
"id": "62230d31",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Last edited: Thursday, April 21 2022 4:40 pm
\n",
"Tasks:
\n",
"1. Incorporate CDO into Python (completed)
\n",
"2. Concatenate yearly data into one temp files (completed)
\n",
"3. Calculate statistics:
\n",
" 3.1. Statistical values over all timesteps
\n",
" 3.2. Multi-year seasonal statistical values
\n",
" 3.3. Multi-year monthly statistical values
\n",
" 3.4. Multy-year hourly statistical values
\n",
"4. Read the output of the above by metview for plotting (completed)
\n",
"5. Plot all months stats (maps) as a panel plot using Metview (completed)
\n",
"6. Plot all months stats (maps) as a panel plot using Matplotlib (pending)
\n",
"7. Define transects (basic logic is done, need implementation)\n",
"8. Regrid input data as needed\n",
"9. Extract values at points of interest, based on the defined transects"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "38af6c2e",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T21:41:27.767836Z",
"start_time": "2022-04-21T21:41:23.057980Z"
}
},
"outputs": [],
"source": [
"import metview as mv\n",
"from cdo import Cdo\n",
"import glob\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bb882e2f",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T21:41:31.198273Z",
"start_time": "2022-04-21T21:41:27.770645Z"
}
},
"outputs": [],
"source": [
"# Initiate CDO\n",
"cdo = Cdo()\n",
"#print(cdo.operators)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b6575ecc",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T21:41:31.204201Z",
"start_time": "2022-04-21T21:41:31.200339Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stl1\n",
"Soil temperature layer 1 (0-7 cm)\n",
"/home/amuttaqin/Datasets/ERA5-Land/stl1/yearly/*.grib\n",
"/home/amuttaqin/Figures/stl1/\n"
]
}
],
"source": [
"# Define variable names\n",
"var = 'stl1'\n",
"desc = 'Soil temperature layer 1 (0-7 cm)'\n",
"pathin = '/home/amuttaqin/Datasets/ERA5-Land/'+var+'/yearly/*.grib'\n",
"pathout = '/home/amuttaqin/Figures/'+var+'/'\n",
"\n",
"print(var)\n",
"print(desc)\n",
"print(pathin)\n",
"print(pathout)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7a2ee3e1",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T21:08:46.557128Z",
"start_time": "2022-04-21T21:08:10.450505Z"
}
},
"outputs": [],
"source": [
"# Concatenate grib files from 1991 to 2020 and save as tempfiles f for further analyses\n",
"#f = cdo.cat(input=glob.glob(pathin), options='-r')\n",
"#cdo.cat(input=glob.glob(pathin), options=\"-r\", output=\"/home/amuttaqin/Temporary/stl1_tmp.grib\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "13de23fb",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T21:41:50.460536Z",
"start_time": "2022-04-21T21:41:43.444472Z"
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"['File format : GRIB',\n",
" '-1 : Institut Source T Steptype Levels Num Points Num Dtype : Parameter ID',\n",
" '1 : ECMWF unknown v instant 1 1 241101 1 P16 : 139.128',\n",
" 'Grid coordinates :',\n",
" '1 : lonlat : points=241101 (801x301)',\n",
" 'lon : 90 to 170 by 0.1 degrees_east',\n",
" 'lat : 15 to -15 by -0.1 degrees_north',\n",
" 'Vertical coordinates :',\n",
" '1 : depth_below_land : levels=1',\n",
" 'depth : 0 cm',\n",
" 'bounds : 0-7 cm',\n",
" 'Time coordinate :',\n",
" 'time : unlimited steps',\n",
" 'RefTime = 1991-01-01 00:00:00 Units = hours Calendar = proleptic_gregorian',\n",
" 'YYYY-MM-DD hh:mm:ss YYYY-MM-DD hh:mm:ss YYYY-MM-DD hh:mm:ss YYYY-MM-DD hh:mm:ss',\n",
" '1991-01-01 00:00:00 1991-01-01 01:00:00 1991-01-01 02:00:00 1991-01-01 03:00:00',\n",
" '1991-01-01 04:00:00 1991-01-01 05:00:00 1991-01-01 06:00:00 1991-01-01 07:00:00',\n",
" '1991-01-01 08:00:00 1991-01-01 09:00:00 1991-01-01 10:00:00 1991-01-01 11:00:00',\n",
" '1991-01-01 12:00:00 1991-01-01 13:00:00 1991-01-01 14:00:00 1991-01-01 15:00:00',\n",
" '1991-01-01 16:00:00 1991-01-01 17:00:00 1991-01-01 18:00:00 1991-01-01 19:00:00',\n",
" '1991-01-01 20:00:00 1991-01-01 21:00:00 1991-01-01 22:00:00 1991-01-01 23:00:00',\n",
" '1991-01-02 00:00:00 1991-01-02 01:00:00 1991-01-02 02:00:00 1991-01-02 03:00:00',\n",
" '1991-01-02 04:00:00 1991-01-02 05:00:00 1991-01-02 06:00:00 1991-01-02 07:00:00',\n",
" '1991-01-02 08:00:00 1991-01-02 09:00:00 1991-01-02 10:00:00 1991-01-02 11:00:00',\n",
" '1991-01-02 12:00:00 1991-01-02 13:00:00 1991-01-02 14:00:00 1991-01-02 15:00:00',\n",
" '1991-01-02 16:00:00 1991-01-02 17:00:00 1991-01-02 18:00:00 1991-01-02 19:00:00',\n",
" '1991-01-02 20:00:00 1991-01-02 21:00:00 1991-01-02 22:00:00 1991-01-02 23:00:00',\n",
" '1991-01-03 00:00:00 1991-01-03 01:00:00 1991-01-03 02:00:00 1991-01-03 03:00:00',\n",
" '1991-01-03 04:00:00 1991-01-03 05:00:00 1991-01-03 06:00:00 1991-01-03 07:00:00',\n",
" '1991-01-03 08:00:00 1991-01-03 09:00:00 1991-01-03 10:00:00 1991-01-03 11:00:00',\n",
" '................................................................................',\n",
" '................................................................................',\n",
" '................................................................................',\n",
" '................................................................................',\n",
" '..................',\n",
" '2020-12-29 12:00:00 2020-12-29 13:00:00 2020-12-29 14:00:00 2020-12-29 15:00:00',\n",
" '2020-12-29 16:00:00 2020-12-29 17:00:00 2020-12-29 18:00:00 2020-12-29 19:00:00',\n",
" '2020-12-29 20:00:00 2020-12-29 21:00:00 2020-12-29 22:00:00 2020-12-29 23:00:00',\n",
" '2020-12-30 00:00:00 2020-12-30 01:00:00 2020-12-30 02:00:00 2020-12-30 03:00:00',\n",
" '2020-12-30 04:00:00 2020-12-30 05:00:00 2020-12-30 06:00:00 2020-12-30 07:00:00',\n",
" '2020-12-30 08:00:00 2020-12-30 09:00:00 2020-12-30 10:00:00 2020-12-30 11:00:00',\n",
" '2020-12-30 12:00:00 2020-12-30 13:00:00 2020-12-30 14:00:00 2020-12-30 15:00:00',\n",
" '2020-12-30 16:00:00 2020-12-30 17:00:00 2020-12-30 18:00:00 2020-12-30 19:00:00',\n",
" '2020-12-30 20:00:00 2020-12-30 21:00:00 2020-12-30 22:00:00 2020-12-30 23:00:00',\n",
" '2020-12-31 00:00:00 2020-12-31 01:00:00 2020-12-31 02:00:00 2020-12-31 03:00:00',\n",
" '2020-12-31 04:00:00 2020-12-31 05:00:00 2020-12-31 06:00:00 2020-12-31 07:00:00',\n",
" '2020-12-31 08:00:00 2020-12-31 09:00:00 2020-12-31 10:00:00 2020-12-31 11:00:00',\n",
" '2020-12-31 12:00:00 2020-12-31 13:00:00 2020-12-31 14:00:00 2020-12-31 15:00:00',\n",
" '2020-12-31 16:00:00 2020-12-31 17:00:00 2020-12-31 18:00:00 2020-12-31 19:00:00',\n",
" '2020-12-31 20:00:00 2020-12-31 21:00:00 2020-12-31 22:00:00 2020-12-31 23:00:00']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Inspect the content of the file\n",
"cdo.sinfo(input=\"/home/amuttaqin/Temporary/stl1_tmp.grib\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "7d8f98c5",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T22:16:53.845418Z",
"start_time": "2022-04-21T22:12:11.296534Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'/home/amuttaqin/Temporary/f_JJA_hr_std.grib'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdo.dhouravg(input=\"-select,season=JJA %s\" % (\"/home/amuttaqin/Temporary/stl1_tmp.grib\"), output=\"/home/amuttaqin/Temporary/f_JJA_hr_avg.grib\")\n",
"cdo.dhourstd(input=\"-select,season=JJA %s\" % (\"/home/amuttaqin/Temporary/stl1_tmp.grib\"), output=\"/home/amuttaqin/Temporary/f_JJA_hr_std.grib\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "1acee7a0",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T22:17:49.917428Z",
"start_time": "2022-04-21T22:17:49.781943Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['-1 : Date Time Level Gridsize Miss : Minimum Mean Maximum : Parameter ID',\n",
" '1 : 2020-08-31 00:00:00 0 241101 209345 : 284.49 297.40 304.42 : 139.128',\n",
" '2 : 2020-08-31 01:00:00 0 241101 209345 : 285.50 298.39 305.39 : 139.128',\n",
" '3 : 2020-08-31 02:00:00 0 241101 209345 : 286.22 299.32 308.11 : 139.128',\n",
" '4 : 2020-08-31 03:00:00 0 241101 209345 : 286.72 300.19 312.08 : 139.128',\n",
" '5 : 2020-08-31 04:00:00 0 241101 209345 : 286.92 300.84 315.02 : 139.128',\n",
" '6 : 2020-08-31 05:00:00 0 241101 209345 : 286.87 301.25 316.58 : 139.128',\n",
" '7 : 2020-08-31 06:00:00 0 241101 209345 : 286.61 301.37 316.31 : 139.128',\n",
" '8 : 2020-08-31 07:00:00 0 241101 209345 : 286.10 301.20 314.19 : 139.128',\n",
" '9 : 2020-08-31 08:00:00 0 241101 209345 : 285.49 300.77 311.18 : 139.128',\n",
" '10 : 2020-08-31 09:00:00 0 241101 209345 : 284.75 300.13 308.86 : 139.128',\n",
" '11 : 2020-08-31 10:00:00 0 241101 209345 : 284.21 299.43 307.73 : 139.128',\n",
" '12 : 2020-08-31 11:00:00 0 241101 209345 : 283.85 298.79 306.31 : 139.128',\n",
" '13 : 2020-08-31 12:00:00 0 241101 209345 : 283.58 298.25 306.02 : 139.128',\n",
" '14 : 2020-08-31 13:00:00 0 241101 209345 : 283.32 297.83 305.83 : 139.128',\n",
" '15 : 2020-08-31 14:00:00 0 241101 209345 : 283.09 297.49 305.66 : 139.128',\n",
" '16 : 2020-08-31 15:00:00 0 241101 209345 : 282.89 297.20 305.49 : 139.128',\n",
" '17 : 2020-08-31 16:00:00 0 241101 209345 : 282.69 296.96 305.34 : 139.128',\n",
" '18 : 2020-08-31 17:00:00 0 241101 209345 : 282.50 296.75 305.20 : 139.128',\n",
" '19 : 2020-08-31 18:00:00 0 241101 209345 : 282.33 296.56 305.06 : 139.128',\n",
" '20 : 2020-08-31 19:00:00 0 241101 209345 : 282.21 296.42 304.93 : 139.128',\n",
" '21 : 2020-08-31 20:00:00 0 241101 209345 : 282.09 296.28 304.81 : 139.128',\n",
" '22 : 2020-08-31 21:00:00 0 241101 209345 : 281.95 296.16 304.69 : 139.128',\n",
" '23 : 2020-08-31 22:00:00 0 241101 209345 : 282.35 296.20 304.58 : 139.128',\n",
" '24 : 2020-08-31 23:00:00 0 241101 209345 : 283.46 296.56 304.47 : 139.128']"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdo.info(input=\"/home/amuttaqin/Temporary/f_JJA_hr_avg.grib\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9ce8f1db",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T21:09:30.214166Z",
"start_time": "2022-04-21T21:09:30.212266Z"
}
},
"outputs": [],
"source": [
"# Recap of lat1, lon1, lat2, lon2, crx, cry, clx, cly, clt.x, clt.y, clb.x, clb.y, crt.x, crt.y, crb.x, crb.y\n",
"# 4.85, 95.4, 3.75, 96.5"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cfa54d4f",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T21:09:32.250918Z",
"start_time": "2022-04-21T21:09:32.238058Z"
}
},
"outputs": [],
"source": [
"from shapely.geometry import LineString\n",
"\n",
"def define_transects(lat1, lon1, lat2, lon2, dist2coast, dist2trnsc):\n",
" # Define transect lines and its coordinates. The lats and longs are needed for extracting \n",
" # the values of a field for further analyses.\n",
" \n",
" slope = (lat2-lat1)/(lon2-lon1)\n",
" \n",
" ab = LineString([(lat1, lon1), (lat2, lon2)])\n",
" \n",
" left = ab.parallel_offset(dist2coast, 'left')\n",
" right = ab.parallel_offset(dist2coast, 'right')\n",
" \n",
" r1 = right.boundary[1]\n",
" r2 = right.boundary[0]\n",
" l1 = left.boundary[1]\n",
" l2 = left.boundary[0]\n",
" \n",
" crx = (r1.x+r2.x)/2\n",
" cry = (r1.y+r2.y)/2\n",
" clx = (l1.x+l2.x)/2\n",
" cly = (l1.y+l2.y)/2\n",
" \n",
" cc = LineString([(crx, cry), (clx, cly)])\n",
" \n",
" cleft = cc.parallel_offset(dist2trnsc, 'left')\n",
" cright = cc.parallel_offset(dist2trnsc, 'right')\n",
" \n",
" clt = cleft.boundary[1]\n",
" clb = cleft.boundary[0]\n",
" crt = cright.boundary[1]\n",
" crb = cright.boundary[0]\n",
" \n",
" return slope, crx, cry, clx, cly, clt.x, clt.y, clb.x, clb.y, crt.x, crt.y, crb.x, crb.y\n",
"\n",
"define_transects(4.85, 95.4, 3.75, 96.5, 0.4, 0.4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2d10a8ef",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T21:09:35.340999Z",
"start_time": "2022-04-21T21:09:35.332775Z"
}
},
"outputs": [],
"source": [
"# Define transects\n",
"\n",
"from shapely.geometry import LineString\n",
"\n",
"lat1 = 4.85\n",
"lon1 = 95.4\n",
"lat2 = 3.75\n",
"lon2 = 96.5\n",
"dist2coast = 0.4\n",
"dist2trnsc = 0.4\n",
"\n",
"slope = (lat2-lat1)/(lon2-lon1)\n",
"print(\"slope = \",slope) \n",
"\n",
"ab = LineString([(lat1, lon1), (lat2, lon2)])\n",
"print(\"Length of transect line parallel to coastline =\", ab.length)\n",
"\n",
"left = ab.parallel_offset(dist2coast, 'left')\n",
"right = ab.parallel_offset(dist2coast, 'right')\n",
"\n",
"r1 = right.boundary[1]\n",
"r2 = right.boundary[0]\n",
"l1 = left.boundary[1]\n",
"l2 = left.boundary[0]\n",
"\n",
"crx = (r1.x+r2.x)/2\n",
"cry = (r1.y+r2.y)/2\n",
"clx = (l1.x+l2.x)/2\n",
"cly = (l1.y+l2.y)/2\n",
"\n",
"cc = LineString([(crx, cry), (clx, cly)])\n",
"\n",
"cleft = cc.parallel_offset(dist2trnsc, 'left')\n",
"cright = cc.parallel_offset(dist2trnsc, 'right')\n",
"\n",
"clt = cleft.boundary[1]\n",
"clb = cleft.boundary[0]\n",
"crt = cright.boundary[1]\n",
"crb = cright.boundary[0]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "201d3781",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T21:08:54.660812Z",
"start_time": "2022-04-21T21:08:52.966298Z"
}
},
"outputs": [],
"source": [
"mv.setoutput(\"jupyter\", plot_widget=False, output_width=1800)\n",
"#mv.setoutput(mv.pdf_output(output_name='/home/amuttaqin/Figures/stl1/trnsct'))\n",
"\n",
"my_view = mv.geoview(\n",
" map_area_definition=\"corners\",\n",
" area=[2,95,6,98])\n",
"\n",
"my_coast = mv.mcoast(\n",
" map_coastline_colour=\"charcoal\",\n",
" map_coastline_resolution=\"high\",\n",
" map_coastline_land_shade=\"off\",\n",
" map_coastline_land_shade_colour=\"beige\",\n",
" map_coastline_sea_shade=\"on\",\n",
" map_coastline_sea_shade_colour=\"RGB(127,205,255)\",\n",
" map_grid_line_style=\"solid\",\n",
" map_grid_latitude_increment=0.1,\n",
" map_grid_longitude_increment=0.1,\n",
" map_label=\"on\",\n",
" map_label_latitude_frequency=5,\n",
" map_label_longitude_frequency=5,\n",
" map_label_height=0.5,\n",
")\n",
"\n",
"pcoast1 = [lat1, lon1, lat2, lon2]\n",
"cl = [clt.x, clt.y, clb.x, clb.y]\n",
"cr = [crt.x, crt.y, crb.x, crb.y]\n",
"cc = [crx, cry, clx, cly]\n",
"\n",
"line_graph = mv.mgraph(\n",
" graph_line_colour = \"red\",\n",
" graph_line_thickness = 5.)\n",
"\n",
"mv.plot(\n",
" my_view,\n",
" my_coast,\n",
" mv.mvl_geoline(*pcoast1,1),\n",
" mv.mvl_geoline(*cr,1),\n",
" mv.mvl_geoline(*cl,1),\n",
" mv.mvl_geoline(*cc,1), line_graph\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a904fcda",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T21:08:54.665600Z",
"start_time": "2022-04-21T21:08:54.662422Z"
}
},
"outputs": [],
"source": [
"print(crx, cry)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "00d028b2",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T21:13:32.736864Z",
"start_time": "2022-04-21T21:09:53.031524Z"
}
},
"outputs": [],
"source": [
"ts = cdo.remapnn('lon=4.58284/lat=96.23284', input=\"/home/amuttaqin/Temporary/stl1_tmp.grib\")\n",
"#ts = cdo.remapnn('lon='+str(crx)+'/lat='+str(cry), input=f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bdfc2124",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T21:15:52.054239Z",
"start_time": "2022-04-21T21:15:51.307909Z"
}
},
"outputs": [],
"source": [
"cdo.sinfo(input=ts)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "57db4cfc",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T21:16:05.822329Z",
"start_time": "2022-04-21T21:16:03.955352Z"
}
},
"outputs": [],
"source": [
"cdo.info(input=ts)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "807f23f5",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T21:16:33.082457Z",
"start_time": "2022-04-21T21:16:28.337972Z"
}
},
"outputs": [],
"source": [
"stl1 = mv.read(\"/home/amuttaqin/Temporary/stl1_tmp.grib\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "186ffc72",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T21:19:43.824940Z",
"start_time": "2022-04-21T21:19:43.822200Z"
}
},
"outputs": [],
"source": [
"#stl1.describe('stl1')\n",
"print(stl1[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bf007a92",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T21:32:28.198789Z",
"start_time": "2022-04-21T21:32:28.190266Z"
},
"scrolled": true
},
"outputs": [],
"source": [
"loc = [97, 4.6]\n",
"#ts_mv = mv.interpolate(stl1[0], loc)\n",
"#mv.interpolate(stl1[1], loc)\n",
"print(mv.nearest_gridpoint_info(stl1[0], loc))\n",
"print(mv.surrounding_points_indexes(stl1[0], loc))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d2f86e96",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T21:17:25.836730Z",
"start_time": "2022-04-21T21:17:25.834728Z"
}
},
"outputs": [],
"source": [
"#ts_mv"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4115b70f",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T19:34:33.992541Z",
"start_time": "2022-04-21T19:34:33.990096Z"
}
},
"outputs": [],
"source": [
"# Calculate further analyses based on the extracted values and plot the results"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6683fdec",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T19:34:36.103161Z",
"start_time": "2022-04-21T19:34:33.994202Z"
}
},
"outputs": [],
"source": [
"# Remove tempfiles\n",
"cdo.cleanTempDir()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6be8fd05",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "97d4e1e1",
"metadata": {},
"source": [
"Thanks to:\n",
"1. Climate Data Operators developer
\n",
"2. Python-cdo developer
\n",
"3. Conda developer"
]
},
{
"cell_type": "markdown",
"id": "4ca48d2a",
"metadata": {},
"source": [
"Backyard"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "89b8bd5b",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T19:34:36.107437Z",
"start_time": "2022-04-21T19:34:36.105512Z"
}
},
"outputs": [],
"source": [
"# Inspect the grid description\n",
"#cdo.griddes(input=f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e79bf3de",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T19:34:36.110356Z",
"start_time": "2022-04-21T19:34:36.108731Z"
}
},
"outputs": [],
"source": [
"# Calculate statistical values over all timesteps (EST 58 mins)\n",
"#f_avg = cdo.timavg(input=f)\n",
"#f_std = cdo.timstd(input=f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a1e84d54",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T19:34:36.113385Z",
"start_time": "2022-04-21T19:34:36.111611Z"
}
},
"outputs": [],
"source": [
"# Calculate seasonal statistical values (EST 4 mins for each season)\n",
"# Select season than calculate dhouravg for that season\n",
"#f_DJF_hr_avg = cdo.dhouravg(input=\"-select,season=DJF %s\" % (f))\n",
"#f_DJF_hr_std = cdo.dhourstd(input=\"-select,season=DJF %s\" % (f))\n",
"\n",
"#f_MAM_hr_avg = cdo.dhouravg(input=\"-select,season=MAM %s\" % (f))\n",
"#f_MAM_hr_std = cdo.dhourstd(input=\"-select,season=MAM %s\" % (f))\n",
"\n",
"#f_JJA_hr_avg = cdo.dhouravg(input=\"-select,season=JJA %s\" % (f))\n",
"#f_JJA_hr_std = cdo.dhourstd(input=\"-select,season=JJA %s\" % (f))\n",
"\n",
"#f_SON_hr_avg = cdo.dhouravg(input=\"-select,season=SON %s\" % (f))\n",
"#f_SON_hr_std = cdo.dhourstd(input=\"-select,season=SON %s\" % (f))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc2d593f",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T19:34:36.116203Z",
"start_time": "2022-04-21T19:34:36.114607Z"
}
},
"outputs": [],
"source": [
"# Calculate Multi-year monthly statistical values\n",
"#f_mon_avg = cdo.ymonavg(input=f)\n",
"#f_mon_std = cdo.ymonstd(input=f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "08240716",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T19:34:36.118985Z",
"start_time": "2022-04-21T19:34:36.117401Z"
}
},
"outputs": [],
"source": [
"# Calculate Multi-day hourly statistical values\n",
"#f_hr_avg = cdo.dhouravg(input=f)\n",
"#f_hr_std = cdo.dhourstd(input=f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62c2d7b7",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T19:34:36.121871Z",
"start_time": "2022-04-21T19:34:36.120151Z"
}
},
"outputs": [],
"source": [
"#whos"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3bae0637",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T19:34:36.124837Z",
"start_time": "2022-04-21T19:34:36.123026Z"
}
},
"outputs": [],
"source": [
"# Inspect the statistics (visual checks)\n",
"#cdo.info(input=f_avg)\n",
"#cdo.info(input=f_std)\n",
"\n",
"#cdo.info(input=f_seas_avg) # weird values\n",
"#cdo.info(input=f_seas_std) # wird nans\n",
"\n",
"#cdo.info(input=f_mon_avg)\n",
"#cdo.info(input=f_mon_std)\n",
"\n",
"#cdo.info(input=f_hr_avg)\n",
"#cdo.info(input=f_hr_std)\n",
"\n",
"#cdo.info(input=f_DJF_hr_avg)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2fb8cc12",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T19:34:36.127685Z",
"start_time": "2022-04-21T19:34:36.126040Z"
}
},
"outputs": [],
"source": [
"# Read the monthly statistcs using Metview for plotting\n",
"#m_avg = mv.read(f_avg)\n",
"#m_std = mv.read(f_std)\n",
"\n",
"#m_seas_avg = mv.read(f_seas_avg)\n",
"#m_seas_std = mv.read(f_seas_std)\n",
"\n",
"#m_mon_avg = mv.read(f_mon_avg)\n",
"#m_mon_std = mv.read(f_mon_std)\n",
"\n",
"#m_hr_avg = mv.read(f_hr_avg)\n",
"#m_hr_std = mv.read(f_hr_std)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bf5d3515",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T19:34:36.173450Z",
"start_time": "2022-04-21T19:34:36.128887Z"
}
},
"outputs": [],
"source": [
"# # Plot for statistical values over all timesteps\n",
"# title = []\n",
"\n",
"# # define coastlines and titles\n",
"# for val in ['Avg', 'Std']:\n",
" \n",
"# title.append(\n",
"# mv.mtext(text_lines=[f\"{val}\"], text_font_size=0.5)\n",
"# )\n",
"\n",
"# # define view\n",
"# view = mv.geoview(\n",
"# map_area_definition=\"corners\", \n",
"# area=[-15.00, 90.00, 15.00, 160.00])\n",
"\n",
"# coast = mv.mcoast(\n",
"# map_coastline_colour=\"charcoal\",\n",
"# map_coastline_resolution=\"medium\",\n",
"# map_coastline_land_shade=\"off\",\n",
"# map_coastline_sea_shade=\"off\",\n",
"# map_grid_line_style=\"dot\",\n",
"# map_label_height=0.35)\n",
"\n",
"# contour = mv.mcont(\n",
"# legend=\"on\",\n",
"# contour=\"off\",\n",
"# contour_min_level=12.0,\n",
"# contour_max_level=36.0,\n",
"# contour_level_count=12,\n",
"# contour_label=\"off\",\n",
"# contour_shade=\"on\",\n",
"# contour_shade_method=\"area_fill\",\n",
"# contour_shade_colour_method=\"palette\",\n",
"# contour_shade_palette_name=\"colorbrewer_RdBu_12\")\n",
"\n",
"# # create a mxn (columns by rows) plot layout with the defined geoview\n",
"# dw = mv.plot_superpage(\n",
"# pages=mv.mvl_regular_layout(view, 1, 2, 1, 1, [1, 99, 1, 99])) # number of grid depends on: all timesteps (1), seas (4), \n",
"# # mon (12), hr (24)\n",
"\n",
"# # define output (pdf vs jupyter)\n",
"# mv.setoutput(mv.pdf_output(output_name=pathout+'overall_stats')) #m_avg, m_std, m_seas_avg, m_seas_std, m_mon_avg, m_mon_std, m_hr_avg, m_hr_std\n",
"# #mv.setoutput(\"jupyter\", plot_widget=False)\n",
"\n",
"# # generate plot\n",
"# mv.plot(\n",
"# dw[0], view, coast, contour, title[0], m_avg[0], #m_avg, m_std, m_seas_avg, m_seas_std, m_mon_avg, m_mon_std, m_hr_avg, m_hr_std,\n",
"# dw[1], view, coast, contour, title[1], m_std[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0ce648ab",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T19:34:36.177202Z",
"start_time": "2022-04-21T19:34:36.174769Z"
}
},
"outputs": [],
"source": [
"# # Plot for Multi-year seasonal statistical values\n",
"# title = []\n",
"\n",
"# # define coastlines and titles\n",
"# for val in ['DJF', 'MAM', 'JJA', 'SON']:\n",
" \n",
"# title.append(\n",
"# mv.mtext(text_lines=[f\"{val}\"], text_font_size=0.5)\n",
"# )\n",
"\n",
"# contour = mv.mcont(\n",
"# legend=\"on\",\n",
"# contour=\"off\",\n",
"# contour_min_level=12.0,\n",
"# contour_max_level=36.0,\n",
"# contour_level_count=12,\n",
"# contour_label=\"off\",\n",
"# contour_shade=\"on\",\n",
"# contour_shade_method=\"area_fill\",\n",
"# contour_shade_colour_method=\"palette\",\n",
"# contour_shade_palette_name=\"colorbrewer_RdBu_12\")\n",
"\n",
"# # create a mxn (columns by rows) plot layout with the defined geoview\n",
"# dw = mv.plot_superpage(\n",
"# pages=mv.mvl_regular_layout(view, 2, 2, 1, 1, [1, 99, 1, 99])) # number of grid depends on: all timesteps (1), seas (4), \n",
"# # mon (12), hr (24)\n",
"\n",
"# # define output (pdf vs jupyter)\n",
"# mv.setoutput(mv.pdf_output(output_name=pathout+'seas_avg_stats')) #m_avg, m_std, m_seas_avg, m_seas_std, m_mon_avg, m_mon_std, m_hr_avg, m_hr_std\n",
"# #mv.setoutput(\"jupyter\", plot_widget=False)\n",
"\n",
"# # generate plot\n",
"# mv.plot(\n",
"# dw[0], view, coast, contour, title[0], m_seas_avg[0], #m_avg, m_std, m_seas_avg, m_seas_std, m_mon_avg, m_mon_std, m_hr_avg, m_hr_std,\n",
"# dw[1], view, coast, contour, title[1], m_seas_avg[1],\n",
"# dw[2], view, coast, contour, title[2], m_seas_avg[2],\n",
"# dw[3], view, coast, contour, title[3], m_seas_avg[3],\n",
"# )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "69a4295c",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T19:34:36.180932Z",
"start_time": "2022-04-21T19:34:36.178529Z"
}
},
"outputs": [],
"source": [
"# # Plot for Multi-year monthly statistical values\n",
"# title = []\n",
"\n",
"# # define coastlines and titles\n",
"# for val in ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']:\n",
" \n",
"# title.append(\n",
"# mv.mtext(text_lines=[f\"{val}\"], text_font_size=0.5)\n",
"# )\n",
"\n",
"# contour = mv.mcont(\n",
"# legend=\"on\",\n",
"# contour=\"off\",\n",
"# contour_min_level=12.0,\n",
"# contour_max_level=36.0,\n",
"# contour_level_count=12,\n",
"# contour_label=\"off\",\n",
"# contour_shade=\"on\",\n",
"# contour_shade_method=\"area_fill\",\n",
"# contour_shade_colour_method=\"palette\",\n",
"# contour_shade_palette_name=\"colorbrewer_RdBu_12\")\n",
"\n",
"# # create a mxn (columns by rows) plot layout with the defined geoview\n",
"# dw = mv.plot_superpage(\n",
"# pages=mv.mvl_regular_layout(view, 3, 4, 1, 1, [1, 99, 1, 99])) # number of grid depends on: all timesteps (1), seas (4), \n",
"# # mon (12), hr (24)\n",
"\n",
"# # define output (pdf vs jupyter)\n",
"# mv.setoutput(mv.pdf_output(output_name='/home/amuttaqin/Figures/stl1/m_avg')) #m_avg, m_std, m_seas_avg, m_seas_std, m_mon_avg, m_mon_std, m_hr_avg, m_hr_std\n",
"# #mv.setoutput(\"jupyter\", plot_widget=False)\n",
"\n",
"# # generate plot\n",
"# mv.plot(\n",
"# dw[0], view, coast, contour, title[0], m_mon_avg[0], #m_avg, m_std, m_seas_avg, m_seas_std, m_mon_avg, m_mon_std, m_hr_avg, m_hr_std,\n",
"# dw[1], view, coast, contour, title[1], m_mon_avg[1],\n",
"# dw[2], view, coast, contour, title[2], m_mon_avg[2],\n",
"# dw[3], view, coast, contour, title[3], m_mon_avg[3],\n",
"# dw[4], view, coast, contour, title[4], m_mon_avg[4],\n",
"# dw[5], view, coast, contour, title[5], m_mon_avg[5],\n",
"# dw[6], view, coast, contour, title[6], m_mon_avg[6],\n",
"# dw[7], view, coast, contour, title[7], m_mon_avg[7],\n",
"# dw[8], view, coast, contour, title[8], m_mon_avg[8],\n",
"# dw[9], view, coast, contour, title[9], m_mon_avg[9],\n",
"# dw[10], view, coast, contour, title[10], m_mon_avg[10],\n",
"# dw[11], view, coast, contour, title[11], m_mon_avg[11],\n",
"# )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "496abc26",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T19:34:36.185251Z",
"start_time": "2022-04-21T19:34:36.182160Z"
}
},
"outputs": [],
"source": [
"# # Plot for Multi-year hourly statistical values\n",
"# title = []\n",
"\n",
"# # define coastlines and titles\n",
"# for val in ['00:00 UTC', '01:00 UTC', '02:00 UTC', '03:00 UTC', '04:00 UTC', '05:00 UTC', '06:00 UTC', \n",
"# '07:00 UTC', '08:00 UTC', '09:00 UTC', '10:00 UTC', '11:00 UTC', '12:00 UTC', '13:00 UTC',\n",
"# '14:00 UTC', '15:00 UTC', '16:00 UTC', '17:00 UTC', '18:00 UTC', '19:00 UTC', '20:00 UTC',\n",
"# '21:00 UTC', '22:00 UTC', '23:00 UTC']:\n",
" \n",
"# title.append(\n",
"# mv.mtext(text_lines=[f\"{val}\"], text_font_size=0.5)\n",
"# )\n",
"\n",
"# contour = mv.mcont(\n",
"# legend=\"on\",\n",
"# contour=\"off\",\n",
"# contour_min_level=12.0,\n",
"# contour_max_level=36.0,\n",
"# contour_level_count=12,\n",
"# contour_label=\"off\",\n",
"# contour_shade=\"on\",\n",
"# contour_shade_method=\"area_fill\",\n",
"# contour_shade_colour_method=\"palette\",\n",
"# contour_shade_palette_name=\"colorbrewer_RdBu_12\")\n",
"\n",
"# # create a mxn (columns by rows) plot layout with the defined geoview\n",
"# dw = mv.plot_superpage(\n",
"# pages=mv.mvl_regular_layout(view, 3, 4, 1, 1, [1, 99, 1, 99])) # number of grid depends on: all timesteps (1), seas (4), \n",
"# # mon (12), hr (24)\n",
"\n",
"# # define output (pdf vs jupyter)\n",
"# mv.setoutput(mv.pdf_output(output_name='/home/amuttaqin/Figures/stl1/m_avg')) #m_avg, m_std, m_seas_avg, m_seas_std, m_mon_avg, m_mon_std, m_hr_avg, m_hr_std\n",
"# #mv.setoutput(\"jupyter\", plot_widget=False)\n",
"\n",
"# # generate plot\n",
"# mv.plot(\n",
"# dw[0], view, coast, contour, title[0], m_mon_avg[0], #m_avg, m_std, m_seas_avg, m_seas_std, m_mon_avg, m_mon_std, m_hr_avg, m_hr_std,\n",
"# dw[1], view, coast, contour, title[1], m_mon_avg[1],\n",
"# dw[2], view, coast, contour, title[2], m_mon_avg[2],\n",
"# dw[3], view, coast, contour, title[3], m_mon_avg[3],\n",
"# dw[4], view, coast, contour, title[4], m_mon_avg[4],\n",
"# dw[5], view, coast, contour, title[5], m_mon_avg[5],\n",
"# dw[6], view, coast, contour, title[6], m_mon_avg[6],\n",
"# dw[7], view, coast, contour, title[7], m_mon_avg[7],\n",
"# dw[8], view, coast, contour, title[8], m_mon_avg[8],\n",
"# dw[9], view, coast, contour, title[9], m_mon_avg[9],\n",
"# dw[10], view, coast, contour, title[10], m_mon_avg[10],\n",
"# dw[11], view, coast, contour, title[11], m_mon_avg[11],\n",
"# )\n",
"\n",
"# newpage(dw)\n",
"# mv.plot(\n",
"# dw[12], view, coast, contour, title[12], m_mon_avg[12], #m_avg, m_std, m_seas_avg, m_seas_std, m_mon_avg, m_mon_std, m_hr_avg, m_hr_std,\n",
"# dw[13], view, coast, contour, title[13], m_mon_avg[13],\n",
"# dw[14], view, coast, contour, title[14], m_mon_avg[14],\n",
"# dw[15], view, coast, contour, title[15], m_mon_avg[15],\n",
"# dw[16], view, coast, contour, title[16], m_mon_avg[16],\n",
"# dw[17], view, coast, contour, title[17], m_mon_avg[17],\n",
"# dw[18], view, coast, contour, title[18], m_mon_avg[18],\n",
"# dw[19], view, coast, contour, title[19], m_mon_avg[19],\n",
"# dw[20], view, coast, contour, title[20], m_mon_avg[20],\n",
"# dw[21], view, coast, contour, title[21], m_mon_avg[21],\n",
"# dw[22], view, coast, contour, title[22], m_mon_avg[22],\n",
"# dw[23], view, coast, contour, title[23], m_mon_avg[23],\n",
"# )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f7a69afb",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T19:34:36.187956Z",
"start_time": "2022-04-21T19:34:36.186472Z"
}
},
"outputs": [],
"source": [
"# Read f as metview fieldset\n",
"\n",
"#mf = mv.read(f)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "b86a8aed",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T22:20:52.336505Z",
"start_time": "2022-04-21T22:20:52.315990Z"
}
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'stl1' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_1511627/2876377270.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;31m# generate plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 36\u001b[0m mv.plot(\n\u001b[0;32m---> 37\u001b[0;31m \u001b[0mdw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mview\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoast\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontour\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstl1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m#m_avg, m_std, m_seas_avg, m_seas_std, m_mon_avg, m_mon_std, m_hr_avg, m_hr_std,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 38\u001b[0m \u001b[0mdw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mview\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoast\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontour\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstl1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m )\n",
"\u001b[0;31mNameError\u001b[0m: name 'stl1' is not defined"
]
}
],
"source": [
"# Plot for Multi-year hourly statistical values\n",
"view = mv.geoview(\n",
" map_area_definition=\"corners\", \n",
" area=[-15.00, 90.00, 15.00, 160.00])\n",
"\n",
"coast = mv.mcoast(\n",
" map_coastline_colour=\"charcoal\",\n",
" map_coastline_resolution=\"high\",\n",
" map_coastline_land_shade=\"off\",\n",
" map_coastline_sea_shade=\"off\",\n",
" map_grid_line_style=\"dot\",\n",
" map_label_height=0.35)\n",
"\n",
"contour = mv.mcont(\n",
" legend=\"on\",\n",
" contour=\"off\",\n",
" contour_min_level=12.0,\n",
" contour_max_level=36.0,\n",
" contour_level_count=12,\n",
" contour_label=\"off\",\n",
" contour_shade=\"on\",\n",
" contour_shade_method=\"area_fill\",\n",
" contour_shade_colour_method=\"palette\",\n",
" contour_shade_palette_name=\"colorbrewer_RdBu_12\")\n",
"\n",
"# create a mxn (columns by rows) plot layout with the defined geoview\n",
"dw = mv.plot_superpage(\n",
" pages=mv.mvl_regular_layout(view, 1, 2, 1, 1, [1, 99, 1, 99])) # number of grid depends on: all timesteps (1), seas (4), \n",
" # mon (12), hr (24)\n",
"\n",
"# define output (pdf vs jupyter)\n",
"#mv.setoutput(mv.pdf_output(output_name='/home/amuttaqin/Figures/stl1/test')) #m_avg, m_std, m_seas_avg, m_seas_std, m_mon_avg, m_mon_std, m_hr_avg, m_hr_std\n",
"mv.setoutput(\"jupyter\", plot_widget=False)\n",
"\n",
"# generate plot\n",
"mv.plot(\n",
" dw[0], view, coast, contour, stl1[0], #m_avg, m_std, m_seas_avg, m_seas_std, m_mon_avg, m_mon_std, m_hr_avg, m_hr_std,\n",
" dw[1], view, coast, contour, stl1[1], \n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "f61c530f",
"metadata": {
"ExecuteTime": {
"end_time": "2022-04-21T22:21:40.470200Z",
"start_time": "2022-04-21T22:21:40.167599Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Plot for Multi-year seasonal statistical values\n",
"view = mv.geoview(\n",
" map_area_definition=\"corners\", \n",
" area=[-15.00, 90.00, 15.00, 160.00])\n",
"\n",
"coast = mv.mcoast(\n",
" map_coastline_colour=\"charcoal\",\n",
" map_coastline_resolution=\"medium\",\n",
" map_coastline_land_shade=\"off\",\n",
" map_coastline_sea_shade=\"off\",\n",
" map_grid_line_style=\"dot\",\n",
" map_label_height=0.35)\n",
"\n",
"contour = mv.mcont(\n",
" legend=\"on\",\n",
" contour=\"off\",\n",
" contour_min_level=12.0,\n",
" contour_max_level=36.0,\n",
" contour_level_count=12,\n",
" contour_label=\"off\",\n",
" contour_shade=\"on\",\n",
" contour_shade_method=\"area_fill\",\n",
" contour_shade_colour_method=\"palette\",\n",
" contour_shade_palette_name=\"colorbrewer_RdBu_12\")\n",
"\n",
"# create a mxn (columns by rows) plot layout with the defined geoview\n",
"dw = mv.plot_superpage(\n",
" pages=mv.mvl_regular_layout(view, 1, 2, 1, 1, [5, 95, 5, 95])) # number of grid depends on: all timesteps (1), seas (4), \n",
" # mon (12), hr (24)\n",
"\n",
"# define output (pdf vs jupyter)\n",
"#mv.setoutput(mv.pdf_output(output_name=\"/home/amuttaqin/Figures/stl1/test_JJA\")) #m_avg, m_std, m_seas_avg, m_seas_std, m_mon_avg, m_mon_std, m_hr_avg, m_hr_std\n",
"mv.setoutput(\"jupyter\", plot_widget=False)\n",
"\n",
"# generate plot\n",
"mv.plot(\n",
" dw[0], view, coast, contour, f_JJA_hr_avg[0], \n",
" dw[1], view, coast, contour, f_JJA_hr_std[0],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "899ea380",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}