notebooks/plot_Regional_maps.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/ipykernel_launcher.py:24: UserWarning: \n",
      "This call to matplotlib.use() has no effect because the backend has already\n",
      "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
      "or matplotlib.backends is imported for the first time.\n",
      "\n",
      "The backend was *originally* set to 'module://ipykernel.pylab.backend_inline' by the following code:\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n",
      "    \"__main__\", mod_spec)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/runpy.py\", line 85, in _run_code\n",
      "    exec(code, run_globals)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/ipykernel_launcher.py\", line 16, in <module>\n",
      "    app.launch_new_instance()\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n",
      "    app.start()\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 486, in start\n",
      "    self.io_loop.start()\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 127, in start\n",
      "    self.asyncio_loop.run_forever()\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/asyncio/base_events.py\", line 421, in run_forever\n",
      "    self._run_once()\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/asyncio/base_events.py\", line 1431, in _run_once\n",
      "    handle._run()\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/asyncio/events.py\", line 145, in _run\n",
      "    self._callback(*self._args)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 117, in _handle_events\n",
      "    handler_func(fileobj, events)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/tornado/stack_context.py\", line 276, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 450, in _handle_events\n",
      "    self._handle_recv()\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 480, in _handle_recv\n",
      "    self._run_callback(callback, msg)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 432, in _run_callback\n",
      "    callback(*args, **kwargs)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/tornado/stack_context.py\", line 276, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n",
      "    return self.dispatch_shell(stream, msg)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n",
      "    handler(stream, idents, msg)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n",
      "    user_expressions, allow_stdin)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n",
      "    res = shell.run_cell(code, store_history=store_history, silent=silent)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n",
      "    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2728, in run_cell\n",
      "    interactivity=interactivity, compiler=compiler, result=result)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2850, in run_ast_nodes\n",
      "    if self.run_code(code, result):\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2910, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"<ipython-input-1-c6b09d7e897d>\", line 20, in <module>\n",
      "    get_ipython().run_line_magic('matplotlib', 'inline')\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2095, in run_line_magic\n",
      "    result = fn(*args,**kwargs)\n",
      "  File \"<decorator-gen-107>\", line 2, in matplotlib\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/IPython/core/magic.py\", line 187, in <lambda>\n",
      "    call = lambda f, *a, **k: f(*a, **k)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/IPython/core/magics/pylab.py\", line 99, in matplotlib\n",
      "    gui, backend = self.shell.enable_matplotlib(args.gui)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2978, in enable_matplotlib\n",
      "    pt.activate_matplotlib(backend)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/IPython/core/pylabtools.py\", line 308, in activate_matplotlib\n",
      "    matplotlib.pyplot.switch_backend(backend)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/matplotlib/pyplot.py\", line 231, in switch_backend\n",
      "    matplotlib.use(newbackend, warn=False, force=True)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/matplotlib/__init__.py\", line 1410, in use\n",
      "    reload(sys.modules['matplotlib.backends'])\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
      "    _bootstrap._exec(spec, module)\n",
      "  File \"/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/matplotlib/backends/__init__.py\", line 16, in <module>\n",
      "    line for line in traceback.format_stack()\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "\n",
    "This code is part of the SIPN2 project focused on improving sub-seasonal to seasonal predictions of Arctic Sea Ice. \n",
    "If you use this code for a publication or presentation, please cite the reference in the README.md on the\n",
    "main page (https://github.com/NicWayand/ESIO). \n",
    "\n",
    "Questions or comments should be addressed to nicway@uw.edu\n",
    "\n",
    "Copyright (c) 2018 Nic Wayand\n",
    "\n",
    "GNU General Public License v3.0\n",
    "\n",
    "\n",
    "'''\n",
    "\n",
    "'''\n",
    "Plot forecast maps with all available models.\n",
    "'''\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload\n",
    "import matplotlib\n",
    "matplotlib.use('Agg')\n",
    "import matplotlib.pyplot as plt, mpld3\n",
    "from collections import OrderedDict\n",
    "import itertools\n",
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "import pandas as pd\n",
    "import struct\n",
    "import os\n",
    "import xarray as xr\n",
    "import glob\n",
    "import datetime\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "import seaborn as sns\n",
    "np.seterr(divide='ignore', invalid='ignore')\n",
    "\n",
    "\n",
    "from esio import EsioData as ed\n",
    "from esio import ice_plot\n",
    "\n",
    "import dask\n",
    "dask.set_options(get=dask.threaded.get)\n",
    "\n",
    "# General plotting settings\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_context(\"talk\", font_scale=.8, rc={\"lines.linewidth\": 2.5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_small_contours(p, thres=10):\n",
    "    for level in p.collections:\n",
    "        for kp,path in reversed(list(enumerate(level.get_paths()))):\n",
    "            # go in reversed order due to deletions!\n",
    "\n",
    "            # include test for \"smallness\" of your choice here:\n",
    "            # I'm using a simple estimation for the diameter based on the\n",
    "            #    x and y diameter...\n",
    "            verts = path.vertices # (N,2)-shape array of contour line coordinates\n",
    "            diameter = np.max(verts.max(axis=0) - verts.min(axis=0))\n",
    "\n",
    "            if diameter<thres: # threshold to be refined for your actual dimensions!\n",
    "                del(level.get_paths()[kp])  # no remove() for Path objects:("
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting Info\n",
    "runType = 'forecast'\n",
    "variables = ['sic'] #, 'hi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialization times to plot\n",
    "cd = datetime.datetime.now()\n",
    "cd = datetime.datetime(cd.year, cd.month, cd.day) # Set hour min sec to 0. \n",
    "init_slice = np.arange(cd - datetime.timedelta(days=70), cd, datetime.timedelta(days=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Forecast times to plot\n",
    "weeks = pd.to_timedelta(np.arange(0,5,1), unit='W')\n",
    "months = pd.to_timedelta(np.arange(2,12,1), unit='M')\n",
    "years = pd.to_timedelta(np.arange(1,2), unit='Y') - np.timedelta64(1, 'D') # need 364 not 365\n",
    "slices = weeks.union(months).union(years).round('1d')\n",
    "da_slices = xr.DataArray(slices, dims=('fore_time'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#############################################################\n",
    "# Load in Data\n",
    "#############################################################\n",
    "E = ed.EsioData.load()\n",
    "\n",
    "ds_region = xr.open_dataset(os.path.join(E.grid_dir, 'sio_2016_mask_Update.nc'))\n",
    "\n",
    "median_ice_fill = xr.open_mfdataset(os.path.join(E.obs_dir, 'NSIDC_0051', 'agg_nc', 'ice_edge.nc')).sic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61.69664483005181\n"
     ]
    }
   ],
   "source": [
    "import timeit\n",
    "start_time = timeit.default_timer()\n",
    "ds_81 = xr.open_mfdataset(E.obs['NSIDC_0081']['sipn_nc']+'/*.nc', concat_dim='time', autoclose=True)#,\n",
    "print(timeit.default_timer() - start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(ds_51.time.min().values, ds_51.time.max().values)\n",
    "# print(ds_81.time.min().values, ds_81.time.max().values)\n",
    "# print(ds_79.time.min().values, ds_79.time.max().values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define models to plot\n",
    "models_2_plot = list(E.model.keys())\n",
    "models_2_plot = [x for x in models_2_plot if x!='piomas'] # remove some models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [-3850000*0.9, 3725000*0.8, -5325000*0.7, 5850000*0.9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting plots...\n",
      "2018-06-05T00:00:00.000000\n",
      "0 days\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/dask/array/core.py:2187: UserWarning: Increasing number of chunks by factor of 26\n",
      "  (nparts / max_parts))\n",
      "/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/dask/array/core.py:2187: UserWarning: Increasing number of chunks by factor of 28\n",
      "  (nparts / max_parts))\n",
      "/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/dask/array/core.py:2187: UserWarning: Increasing number of chunks by factor of 31\n",
      "  (nparts / max_parts))\n",
      "/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/dask/array/core.py:2187: UserWarning: Increasing number of chunks by factor of 30\n",
      "  (nparts / max_parts))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found obs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/dask/array/core.py:2187: UserWarning: Increasing number of chunks by factor of 26\n",
      "  (nparts / max_parts))\n",
      "/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/dask/array/core.py:2187: UserWarning: Increasing number of chunks by factor of 28\n",
      "  (nparts / max_parts))\n",
      "/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/dask/array/core.py:2187: UserWarning: Increasing number of chunks by factor of 31\n",
      "  (nparts / max_parts))\n",
      "/home/disk/sipn/nicway/anaconda3/envs/esio/lib/python3.6/site-packages/dask/array/core.py:2187: UserWarning: Increasing number of chunks by factor of 30\n",
      "  (nparts / max_parts))\n"
     ]
    }
   ],
   "source": [
    "# Get # of models and setup subplot dims\n",
    "Nmod = len(E.model.keys()) + 1 #(+1 for obs)\n",
    "Nr = int(np.floor(np.sqrt(Nmod)))\n",
    "Nc = int(np.ceil(Nmod/Nr))\n",
    "assert Nc*Nr>=Nmod, 'Need more subplots'\n",
    "\n",
    "for cvar in variables:\n",
    "    \n",
    "    fig_dir = os.path.join(E.fig_dir, 'model', 'all_model', cvar, \"Regional_maps\")\n",
    "    if not os.path.exists(fig_dir):\n",
    "        os.makedirs(fig_dir)\n",
    "\n",
    "    # Set up plotting info\n",
    "    if cvar=='sic':\n",
    "        cmap_c = matplotlib.colors.ListedColormap(sns.color_palette(\"Blues_r\", 10))\n",
    "        cmap_c.set_bad(color = 'lightgrey')\n",
    "        c_label = 'Sea Ice Concentration (-)'\n",
    "        c_vmin = 0\n",
    "        c_vmax = 1\n",
    "    elif cvar=='hi':\n",
    "        cmap_c = matplotlib.colors.ListedColormap(sns.color_palette(\"Reds_r\", 10))\n",
    "        cmap_c.set_bad(color = 'lightgrey')\n",
    "        c_label = 'Sea Ice Thickness (m)'\n",
    "        c_vmin = 0\n",
    "        c_vmax = None\n",
    "    else:\n",
    "        raise ValueError(\"cvar not found.\")\n",
    "             \n",
    "    print(\"Starting plots...\")\n",
    "    for it in np.flip(init_slice,axis=0): # Flip it here so plots backwards in time\n",
    "        print(it)\n",
    "        \n",
    "        for (cs, ft) in enumerate(da_slices.values):\n",
    "            print(ft.astype('timedelta64[D]'))\n",
    "            cs_str = format(cs, '02')\n",
    "            \n",
    "            # For each region\n",
    "            for cR in ds_region.ocean_regions.values:\n",
    "                \n",
    "                # Get regional extent to plot\n",
    "                crExt = ds_region.where(ds_region.mask==cR, drop=True)[['xm','ym']]\n",
    "                crExt = [crExt.xm.min().values, crExt.xm.max().values, \n",
    "                         crExt.ym.min().values, crExt.ym.max().values]\n",
    "\n",
    "                # New Plot\n",
    "                (f, axes) = ice_plot.multi_polar_axis(ncols=Nc, nrows=Nr, \n",
    "                                                  Nplots=Nmod, extent=crExt)\n",
    "                p = None # initlaize to know if we found any data\n",
    "                for (i, cmod) in enumerate(models_2_plot):\n",
    "                    i = i+1 # shift for obs\n",
    "                    axes[i].set_title(E.model[cmod]['model_label'])\n",
    "\n",
    "                    # Load in Model\n",
    "                    # TODO: try filtering file list by year and month\n",
    "                    model_forecast = os.path.join(E.model[cmod][runType]['sipn_nc'], '*.nc') \n",
    "\n",
    "                    # Check we have files \n",
    "                    files = glob.glob(model_forecast)\n",
    "                    if not files:\n",
    "                        continue # Skip this model\n",
    "                        \n",
    "                    ds_model = xr.open_mfdataset(model_forecast, \n",
    "                                chunks={'fore_time': 1, 'init_time': 1, 'nj': 304, 'ni': 448},\n",
    "                                                concat_dim='init_time')\n",
    "                    ds_model.rename({'nj':'x', 'ni':'y'}, inplace=True)\n",
    "                    \n",
    "                   # Select init and fore_time of interest\n",
    "                    if it in ds_model.init_time.values:\n",
    "    #                     print('found init_time')\n",
    "                        ds_model = ds_model.sel(init_time=it)\n",
    "                    else:\n",
    "                        continue\n",
    "                    \n",
    "                    # Select var of interest (if available)\n",
    "                    if cvar in ds_model.variables:\n",
    "    #                     print('found ',cvar)\n",
    "                        ds_model = ds_model[cvar]\n",
    "                    else:\n",
    "                        continue\n",
    "\n",
    "\n",
    "                    if ft in ds_model.fore_time.values:\n",
    "    #                     print('found fore_time')\n",
    "                        ds_model = ds_model.sel(fore_time=ft)\n",
    "                    else:\n",
    "                        continue\n",
    "                    \n",
    "                    \n",
    "                    # Select region\n",
    "                    # Lat and Long feilds have round off differences, so set to same here\n",
    "                    ds_model['lat'] = ds_region.lat\n",
    "                    ds_model['lon'] = ds_region.lon\n",
    "                    ds_model = ds_model.where(ds_region.mask==cR)\n",
    "\n",
    "                    # Average over ensembles (if available)\n",
    "                    ds_model = ds_model.mean(dim='ensemble')\n",
    "\n",
    "                    # Plot\n",
    "                    p = ds_model.plot.pcolormesh(ax=axes[i], x='lon', y='lat', \n",
    "                                          transform=ccrs.PlateCarree(),\n",
    "                                          add_colorbar=False,\n",
    "                                          cmap=cmap_c,\n",
    "                                          vmin=c_vmin, vmax=c_vmax)\n",
    "\n",
    "\n",
    "                    # Overlay median ice edge\n",
    "#                     cdoy = pd.to_datetime(it + ft).timetuple().tm_yday\n",
    "#                     po = median_ice_fill.isel(time=cdoy).plot.contour(ax=axes[i], x='xm', y='ym', \n",
    "#                                                                       colors=('#bc0f60'),\n",
    "#                                                                       linewidths=[0.5],\n",
    "#                                                                       levels=[0.5], label='Median ice edge 1981-2010')\n",
    "#                     remove_small_contours(po, thres=10)\n",
    "\n",
    "                    axes[i].set_title(E.model[cmod]['model_label'])\n",
    "\n",
    "                # Plot Obs (if available)\n",
    "                if (it + ft) in ds_81.time.values:\n",
    "                    print('found obs')\n",
    "                    reg_ds_81 = ds_81.where(ds_region.mask==cR)\n",
    "                    reg_ds_81.sic.sel(time=(it + ft)).plot.pcolormesh(ax=axes[0], x='lon', y='lat', \n",
    "                                          transform=ccrs.PlateCarree(),\n",
    "                                          add_colorbar=False,\n",
    "                                          cmap=cmap_c,\n",
    "                                          vmin=c_vmin, vmax=c_vmax)\n",
    "                axes[0].set_title('Observed')\n",
    "\n",
    "\n",
    "                # Make pretty\n",
    "                f.subplots_adjust(right=0.8)\n",
    "                cbar_ax = f.add_axes([0.85, 0.15, 0.05, 0.7])\n",
    "                if p:\n",
    "                    cbar = f.colorbar(p, cax=cbar_ax, label=c_label)\n",
    "                    cbar.set_ticks(np.arange(0,1.1,0.1))\n",
    "                    #cbar.set_ticklabels(np.arange(0,1,0.05))\n",
    "\n",
    "                # Set title of all plots\n",
    "                init_time =  pd.to_datetime(it).strftime('%Y-%m-%d-%H:%M')\n",
    "                valid_time = pd.to_datetime(it+ft).strftime('%Y-%m-%d-%H:%M')\n",
    "                plt.suptitle('Initialization Time: '+init_time+'\\n Valid Time: '+valid_time, fontsize=20)\n",
    "                plt.subplots_adjust(top=0.85)\n",
    "\n",
    "                # Save to file\n",
    "                f_out = os.path.join(fig_dir,'Region_'+str(cR)+'_EnsembleMean_'+runType+'_'+init_time+'_'+cs_str+'.png')\n",
    "                f.savefig(f_out,bbox_inches='tight',dpi=200)\n",
    "                #print(\"saved \", f_out)\n",
    "\n",
    "                # Mem clean up\n",
    "                plt.close(f)\n",
    "                p = None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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