{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Notebook for developing functions in analyze.py" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# figures.py imports\n", "from __future__ import division\n", "\n", "#from cStringIO import StringIO\n", "import datetime\n", "import glob\n", "import os\n", "\n", "import arrow\n", "from dateutil import tz\n", "import matplotlib.dates as mdates\n", "import matplotlib.gridspec as gridspec\n", "import matplotlib.pyplot as plt\n", "import matplotlib.cm as cm\n", "import netCDF4 as nc\n", "import numpy as np\n", "import pandas as pd\n", "import requests\n", "from scipy import interpolate as interp\n", "\n", "from salishsea_tools import (\n", " nc_tools,\n", " viz_tools,\n", " stormtools,\n", " tidetools,\n", ")\n", "\n", "#from salishsea_tools.nowcast import figures\n", "#from salishsea_tools.nowcast import analyze\n", "#from salishsea_tools.nowcast import residuals\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "t_orig=datetime.datetime(2015, 1, 22); t_final=datetime.datetime(2015, 1, 29)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bathy = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Constants" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "paths = {'nowcast': '/data/dlatorne/MEOPAR/SalishSea/nowcast/',\n", " 'forecast': '/ocean/sallen/allen/research/MEOPAR/SalishSea/forecast/',\n", " 'forecast2': '/ocean/sallen/allen/research/MEOPAR/SalishSea/forecast2/'}" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "colours = {'nowcast': 'DodgerBlue',\n", " 'forecast': 'ForestGreen',\n", " 'forecast2': 'MediumVioletRed',\n", " 'observed': 'Indigo',\n", " 'predicted': 'ForestGreen',\n", " 'model': 'blue',\n", " 'residual': 'DimGray'}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Functions in module" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def create_path(mode, t_orig, file_part):\n", " \"\"\" Creates a path to a file associated with a simulation for date t_orig. \n", " E.g. create_path('nowcast',datatime.datetime(2015,1,1), 'SalishSea_1h*grid_T.nc') gives\n", " /data/dlatorne/MEOPAR/SalishSea/nowcast/01jan15/SalishSea_1h_20150101_20150101_grid_T.nc\n", "\n", " :arg mode: Mode of results - nowcast, forecast, forecast2.\n", " :type mode: string\n", "\n", " :arg t_orig: The simulation start date.\n", " :type t_orig: datetime object\n", "\n", "\n", " :arg file_part: Identifier for type of file. E.g. SalishSea_1h*grif_T.nc or ssh*.txt\n", " :type grid: string\n", "\n", " :returns: filename, run_date \n", " filename is the full path of the file or an empty list if the file does not exist.\n", " run_date is a datetime object that represents the date the simulation ran\n", " \"\"\"\n", "\n", " run_date = t_orig\n", "\n", " if mode == 'nowcast':\n", " results_home = paths['nowcast']\n", " elif mode == 'forecast':\n", " results_home = paths['forecast']\n", " run_date = run_date + datetime.timedelta(days=-1)\n", " elif mode == 'forecast2':\n", " results_home = paths['forecast2']\n", " run_date = run_date + datetime.timedelta(days=-2)\n", "\n", " results_dir = os.path.join(results_home,\n", " run_date.strftime('%d%b%y').lower())\n", " \n", " filename = glob.glob(os.path.join(results_dir, file_part))\n", " \n", " try: \n", " filename = filename[-1]\n", " except IndexError:\n", " pass\n", "\n", " return filename, run_date" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "('/ocean/sallen/allen/research/MEOPAR/SalishSea/forecast2/20jan15/SalishSea_1h_20150122_20150123_grid_W.nc',\n", " datetime.datetime(2015, 1, 20, 0, 0))" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "create_path('forecast2', t_orig, 'SalishSea*.nc')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def verified_runs(t_orig):\n", " \"\"\" Compiles a list of run types (nowcast, forecast, and/or forecast 2)\n", " that have been verified as complete by checking if their corresponding\n", " .nc files for that day (generated by create_path) exist.\n", "\n", " :arg t_orig: \n", " :type t_orig: datetime object\n", "\n", " :returns: runs_list, list strings representing the runs that completed \n", " \"\"\"\n", "\n", " runs_list = []\n", " for mode in ['nowcast', 'forecast', 'forecast2']:\n", " files, run_date = create_path(mode, t_orig, 'SalishSea*grid_T.nc')\n", " if files:\n", " runs_list.append(mode)\n", "\n", " return runs_list" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def truncate_data(data,time, sdt, edt):\n", " \"\"\" Truncates data for a desired time range: sdt <= time <= edt\n", " data and time must be numpy arrays. \n", " sdt, edt, and times in time must all have a timezone or all be naive.\n", " \n", " :arg data: the data to be truncated\n", " :type data: numpy array\n", " \n", " :arg time: array of times associated with data\n", " :type time: numpy array\n", " \n", " :arg sdt: the start time of the tuncation\n", " :type sdt: datetime object\n", " \n", " :arg edt: the end time of the truncation\n", " :type edt: datetime object\n", " \n", " :returns: data_trun, time_trun, the truncated data and time arrays\n", " \"\"\"\n", "\n", " inds = np.where(np.logical_and(time <=edt, time >=sdt))\n", " \n", " return data[inds], time[inds]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def calculate_residual(ssh, time_ssh, tides, time_tides):\n", " \"\"\" Calculates the residual of the model sea surface height or\n", " observed water levels with respect to the predicted tides.\n", " \n", " :arg ssh: Sea surface height (observed or modelled).\n", " :type ssh: numpy array\n", " \n", " :arg time_ssh: Time component for sea surface height (observed or modelled)\n", " :type time_ssh: numpy array\n", " \n", " :arg tides: Predicted tides.\n", " :type tides: dataFrame object\n", " \n", " :arg time_tides: Time component for predicted tides.\n", " :type time_tides: dataFrame object\n", " \n", " :returns: res, the residual\n", " \"\"\"\n", " \n", " tides_interp = figures.interp_to_model_time(time_ssh, tides, time_tides)\n", " res = ssh - tides_interp\n", " \n", " return res" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_residual_forcing(ax, runs_list, t_orig):\n", " \"\"\" Plots the observed water level residual at Neah Bay against \n", " forced residuals from existing ssh*.txt files for Neah Bay. \n", " Function may produce none, any, or all (nowcast, forecast, forecast 2)\n", " forced residuals depending on availability for specified date (runs_list).\n", "\n", " :arg ax: The axis where the residuals are plotted.\n", " :type ax: axis object\n", " \n", " :arg runs_list: Runs that are verified as complete.\n", " :type runs_list: list\n", " \n", " :arg t_orig: Date being considered.\n", " :type t_orig: datetime object\n", "\n", " \"\"\"\n", " \n", " # truncation times \n", " sdt = t_orig.replace(tzinfo=tz.tzutc())\n", " edt = sdt + datetime.timedelta(days=1)\n", " \n", " # retrieve observations, tides and residual\n", " start_date = t_orig.strftime('%d-%b-%Y'); end_date = start_date\n", " stn_no = figures.SITES['Neah Bay']['stn_no']\n", " obs = figures.get_NOAA_wlevels(stn_no, start_date, end_date)\n", " tides = figures.get_NOAA_tides(stn_no, start_date, end_date)\n", " res_obs = calculate_residual(obs.wlev, obs.time, tides.pred, tides.time)\n", " # truncate and plot\n", " res_obs_trun, time_trun = truncate_data(np.array(res_obs),np.array(obs.time), sdt, edt)\n", " ax.plot(time_trun, res_obs_trun, colours['observed'], label='observed',\n", " linewidth=2.5)\n", "\n", " # plot forcing for each simulation\n", " for mode in runs_list:\n", " filename_NB, run_date = create_path(mode, t_orig, 'ssh*.txt')\n", " if filename_NB:\n", " data = residuals._load_surge_data(filename_NB)\n", " surge, dates = residuals._retrieve_surge(data, run_date)\n", " surge_t, dates_t = truncate_data(np.array(surge),np.array(dates),sdt,edt)\n", " ax.plot(dates_t, surge_t, label=mode, linewidth=2.5,\n", " color=colours[mode]) \n", " ax.set_title('Comparison of observed and forced sea surface height residuals at Neah Bay:'\n", " '{t_forcing:%d-%b-%Y}'.format(t_forcing=t_orig))\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_residual_model(axs, names, runs_list, grid_B, t_orig):\n", " \"\"\" Plots the observed sea surface height residual against the\n", " sea surface height model residual (calculate_residual) at \n", " specified stations. Function may produce none, any, or all \n", " (nowcast, forecast, forecast 2) model residuals depending on \n", " availability for specified date (runs_list).\n", " \n", " :arg ax: The axis where the residuals are plotted.\n", " :type ax: list of axes\n", " \n", " :arg names: Names of station.\n", " :type names: list of names\n", " \n", " :arg runs_list: Runs that have been verified as complete.\n", " :type runs_list: list\n", " \n", " :arg grid_B: Bathymetry dataset for the SalishSeaCast NEMO model.\n", " :type grid_B: :class:`netCDF4.Dataset`\n", " \n", " :arg t_orig: Date being considered.\n", " :type t_orig: datetime object\n", "\n", " \"\"\"\n", "\n", " bathy, X, Y = tidetools.get_bathy_data(grid_B)\n", " t_orig_obs = t_orig + datetime.timedelta(days=-1)\n", " t_final_obs = t_orig + datetime.timedelta(days=1)\n", "\n", " # truncation times \n", " sdt = t_orig.replace(tzinfo=tz.tzutc())\n", " edt = sdt + datetime.timedelta(days=1)\n", " \n", " for ax, name in zip(axs, names):\n", " lat = figures.SITES[name]['lat']; lon = figures.SITES[name]['lon']; msl = figures.SITES[name]['msl']\n", " j, i = tidetools.find_closest_model_point(lon, lat, X, Y, bathy, allow_land=False)\n", " ttide = figures.get_tides(name)\n", " wlev_meas = figures.load_archived_observations(name, t_orig_obs.strftime('%d-%b-%Y'), t_final_obs.strftime('%d-%b-%Y'))\n", " res_obs = calculate_residual(wlev_meas.wlev, wlev_meas.time, ttide.pred_all + msl, ttide.time) \n", " # truncate and plot\n", " res_obs_trun, time_obs_trun = truncate_data(np.array(res_obs), np.array(wlev_meas.time), sdt, edt)\n", " ax.plot(time_obs_trun, res_obs_trun, color=colours['observed'], linewidth=2.5, label='observed')\n", " \n", " for mode in runs_list:\n", " filename, run_date = create_path(mode, t_orig, 'SalishSea_1h_*_grid_T.nc')\n", " grid_T = nc.Dataset(filename)\n", " ssh_loc = grid_T.variables['sossheig'][:, j, i]\n", " t_start, t_final, t_model = figures.get_model_time_variables(grid_T) \n", " res_mod = calculate_residual(ssh_loc, t_model, ttide.pred_8, ttide.time)\n", " # truncate and plot\n", " res_mod_trun, t_mod_trun = truncate_data(res_mod, t_model, sdt, edt)\n", " ax.plot(t_mod_trun, res_mod_trun, label=mode, color=colours[mode], linewidth=2.5)\n", "\n", " ax.set_title('Comparison of modelled sea surface height residuals at {station}: {t:%d-%b-%Y}'.format(station=name, t=t_orig))\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def calculate_error(res_mod, time_mod, res_obs, time_obs):\n", " \"\"\" Calculates the model or forcing residual error.\n", " \n", " :arg res_mod: Residual for model ssh or NB surge data.\n", " :type res_mod: numpy array\n", " \n", " :arg time_mod: Time of model output.\n", " :type time_mod: numpy array\n", " \n", " :arg res_obs: Observed residual (archived or at Neah Bay)\n", " :type res_obs: numpy array\n", " \n", " :arg time_obs: Time corresponding to observed residual.\n", " :type time_obs: numpy array\n", " \n", " :return: error\n", " \"\"\"\n", " \n", " res_obs_interp = figures.interp_to_model_time(time_mod, res_obs, time_obs)\n", " error = res_mod - res_obs_interp\n", " \n", " return error" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def calculate_error_model(names, runs_list, grid_B, t_orig):\n", " \"\"\" Sets up the calculation for the model residual error.\n", " \n", " :arg names: Names of station.\n", " :type names: list of strings\n", " \n", " :arg runs_list: Runs that have been verified as complete.\n", " :type runs_list: list\n", " \n", " :arg grid_B: Bathymetry dataset for the SalishSeaCast NEMO model.\n", " :type grid_B: :class:`netCDF4.Dataset`\n", " \n", " :arg t_orig: Date being considered.\n", " :type t_orig: datetime object\n", " \n", " :returns: error_mod_dict, t_mod_dict, t_orig_dict\n", " \"\"\"\n", " \n", " bathy, X, Y = tidetools.get_bathy_data(grid_B)\n", " t_orig_obs = t_orig + datetime.timedelta(days=-1)\n", " t_final_obs = t_orig + datetime.timedelta(days=1)\n", " \n", " # truncation times \n", " sdt = t_orig.replace(tzinfo=tz.tzutc())\n", " edt = sdt + datetime.timedelta(days=1)\n", " \n", " error_mod_dict = {}; t_mod_dict = {}; t_orig_dict = {}\n", " for name in names:\n", " error_mod_dict[name] = {}; t_mod_dict[name] = {}; t_orig_dict[name] = {}\n", " lat = figures.SITES[name]['lat']; lon = figures.SITES[name]['lon']; msl = figures.SITES[name]['msl']\n", " j, i = tidetools.find_closest_model_point(lon, lat, X, Y, bathy, allow_land=False)\n", " ttide = figures.get_tides(name)\n", " wlev_meas = figures.load_archived_observations(name, t_orig_obs.strftime('%d-%b-%Y'), t_final_obs.strftime('%d-%b-%Y'))\n", " res_obs = calculate_residual(wlev_meas.wlev, wlev_meas.time, ttide.pred_all + msl, ttide.time)\n", " \n", " for mode in runs_list:\n", " filename, run_date = create_path(mode, t_orig, 'SalishSea_1h_*_grid_T.nc')\n", " grid_T = nc.Dataset(filename)\n", " ssh_loc = grid_T.variables['sossheig'][:, j, i]\n", " t_start, t_final, t_model = figures.get_model_time_variables(grid_T)\n", " res_mod = calculate_residual(ssh_loc, t_model, ttide.pred_8, ttide.time)\n", " # truncate \n", " res_mod_trun, t_mod_trun = truncate_data(res_mod, t_model, sdt, edt)\n", " error_mod = calculate_error(res_mod_trun, t_mod_trun, res_obs, wlev_meas.time)\n", " error_mod_dict[name][mode] = error_mod; t_mod_dict[name][mode] = t_mod_trun; t_orig_dict[name][mode] = t_orig\n", " \n", " return error_mod_dict, t_mod_dict, t_orig_dict" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def calculate_error_forcing(name, runs_list, t_orig):\n", " \"\"\" Sets up the calculation for the forcing residual error.\n", " \n", " :arg names: Name of station.\n", " :type names: string\n", " \n", " :arg runs_list: Runs that have been verified as complete.\n", " :type runs_list: list\n", " \n", " :arg t_orig: Date being considered.\n", " :type t_orig: datetime object\n", " \n", " :returns: error_frc_dict, t_frc_dict\n", " \"\"\"\n", " \n", " # truncation times \n", " sdt = t_orig.replace(tzinfo=tz.tzutc())\n", " edt = sdt + datetime.timedelta(days=1)\n", " \n", " # retrieve observed residual\n", " start_date = t_orig.strftime('%d-%b-%Y'); end_date = start_date\n", " stn_no = figures.SITES['Neah Bay']['stn_no']\n", " obs = figures.get_NOAA_wlevels(stn_no, start_date, end_date)\n", " tides = figures.get_NOAA_tides(stn_no, start_date, end_date)\n", " res_obs_NB = calculate_residual(obs.wlev, obs.time, tides.pred, tides.time)\n", " \n", " # calculate forcing error\n", " error_frc_dict = {}; t_frc_dict = {}; error_frc_dict[name] = {}; t_frc_dict[name] = {}\n", " for mode in runs_list:\n", " filename_NB, run_date = create_path(mode, t_orig, 'ssh*.txt')\n", " if filename_NB:\n", " data = residuals._load_surge_data(filename_NB)\n", " surge, dates = residuals._retrieve_surge(data, run_date)\n", " surge_t, dates_t = truncate_data(np.array(surge),np.array(dates), sdt, edt) \n", " error_frc = calculate_error(surge_t, dates_t, res_obs_NB, obs.time)\n", " error_frc_dict[name][mode] = error_frc; t_frc_dict[name][mode] = dates_t\n", " \n", " return error_frc_dict, t_frc_dict" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_error_model(axs, names, runs_list, grid_B, t_orig):\n", " \"\"\" Plots the model residual error. \n", " \n", " :arg axs: The axis where the residual errors are plotted.\n", " :type axs: list of axes\n", " \n", " :arg names: Names of station.\n", " :type names: list of strings\n", " \n", " :arg runs_list: Runs that have been verified as complete.\n", " :type runs_list: list of strings\n", " \n", " :arg grid_B: Bathymetry dataset for the SalishSeaCast NEMO model.\n", " :type grid_B: :class:`netCDF4.Dataset`\n", " \n", " :arg t_orig: Date being considered.\n", " :type t_orig: datetime object\n", "\n", " \"\"\"\n", " \n", " error_mod_dict, t_mod_dict, t_orig_dict = calculate_error_model(names, runs_list, grid_B, t_orig)\n", " \n", " for ax, name in zip(axs, names):\n", " ax.set_title('Comparison of modelled residual errors at {station}: {t:%d-%b-%Y}'.format(station=name, t=t_orig))\n", " for mode in runs_list:\n", " ax.plot(t_mod_dict[name][mode], error_mod_dict[name][mode], label=mode, color=colours[mode], linewidth=2.5)\n", " " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_error_forcing(ax, runs_list, t_orig):\n", " \"\"\" Plots the forcing residual error.\n", " \n", " :arg ax: The axis where the residual errors are plotted.\n", " :type ax: axis object\n", " \n", " :arg runs_list: Runs that have been verified as complete.\n", " :type runs_list: list\n", " \n", " :arg t_orig: Date being considered.\n", " :type t_orig: datetime object\n", " \n", " \"\"\"\n", " \n", " name = 'Neah Bay'\n", " error_frc_dict, t_frc_dict = calculate_error_forcing(name, runs_list, t_orig)\n", "\n", " for mode in runs_list:\n", " ax.plot(t_frc_dict[name][mode], error_frc_dict[name][mode], label=mode, color=colours[mode], linewidth=2.5)\n", " ax.set_title('Comparison of observed and forced residual errors at Neah Bay: {t_forcing:%d-%b-%Y}'.format(t_forcing=t_orig))\n", " " ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_residual_error_all(subject ,grid_B, t_orig, figsize=(20,16)):\n", " \"\"\" Sets up and combines the plots produced by plot_residual_forcing\n", " and plot_residual_model or plot_error_forcing and plot_error_model.\n", " This function specifies the stations for which the nested functions \n", " apply. Figure formatting except x-axis limits and titles are included.\n", " \n", " :arg subject: Subject of figure, either 'residual' or 'error' for residual error.\n", " :type subject: string\n", " \n", " :arg grid_B: Bathymetry dataset for the SalishSeaCast NEMO model.\n", " :type grid_B: :class:`netCDF4.Dataset`\n", " \n", " :arg t_orig: Date being considered.\n", " :type t_orig: datetime object\n", " \n", " :arg figsize: Figure size (width, height) in inches.\n", " :type figsize: 2-tuple\n", " \n", " :returns: fig\n", " \"\"\"\n", " # set up axis limits - based on full 24 hour period 0000 to 2400\n", " sax = t_orig\n", " eax = t_orig +datetime.timedelta(days=1)\n", " \n", " runs_list = verified_runs(t_orig)\n", " \n", " fig, axes = plt.subplots(4, 1, figsize=figsize)\n", " axs_mod = [axes[1], axes[2], axes[3]]\n", " names = ['Point Atkinson', 'Victoria', 'Campbell River']\n", " \n", " if subject == 'residual':\n", " plot_residual_forcing(axes[0], runs_list, t_orig) \n", " plot_residual_model(axs_mod, names, runs_list, grid_B, t_orig)\n", " elif subject == 'error':\n", " plot_error_forcing(axes[0], runs_list, t_orig) \n", " plot_error_model(axs_mod, names, runs_list, grid_B, t_orig)\n", " \n", " for ax in axes:\n", " ax.set_ylim([-0.4, 0.4])\n", " ax.set_xlabel('[hrs UTC]')\n", " ax.set_ylabel('[m]')\n", " hfmt = mdates.DateFormatter('%m/%d %H:%M')\n", " ax.xaxis.set_major_formatter(hfmt)\n", " ax.legend(loc=2, ncol=4)\n", " ax.grid() \n", " ax.set_xlim([sax,eax])\n", " \n", " return fig" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def compare_errors(name, mode, start, end, grid_B, figsize=(20,12)):\n", " \"\"\" compares the model and forcing error at a station between dates start and end\n", " for a simulation mode.\"\"\"\n", " \n", " # array of dates for iteration\n", " numdays = (end-start).days\n", " dates = [start + datetime.timedelta(days=num)\n", " for num in range(0, numdays+1)]\n", " dates.sort()\n", " \n", " # intiialize figure and arrays\n", " fig,axs = plt.subplots(3,1,figsize=figsize)\n", " e_frc=np.array([])\n", " t_frc=np.array([])\n", " e_mod=np.array([]) \n", " t_mod=np.array([])\n", " # mean daily error\n", " frc_daily= np.array([])\n", " mod_daily = np.array([])\n", " t_daily = np.array([])\n", " \n", " ttide=figures.get_tides(name)\n", " \n", " for t_sim in dates:\n", " # check if the run happened\n", " if mode in verified_runs(t_sim):\n", " # retrieve forcing and model error\n", " e_frc_tmp, t_frc_tmp = calculate_error_forcing('Neah Bay', [mode], t_sim)\n", " e_mod_tmp, t_mod_tmp, _ = calculate_error_model([name], [mode], grid_B, t_sim)\n", " e_frc_tmp= figures.interp_to_model_time(t_mod_tmp[name][mode],e_frc_tmp['Neah Bay'][mode],t_frc_tmp['Neah Bay'][mode])\n", " # append to larger array\n", " e_frc = np.append(e_frc,e_frc_tmp)\n", " t_frc = np.append(t_frc,t_mod_tmp[name][mode])\n", " e_mod = np.append(e_mod,e_mod_tmp[name][mode])\n", " t_mod = np.append(t_mod,t_mod_tmp[name][mode])\n", " # append daily mean error\n", " frc_daily=np.append(frc_daily, np.mean(e_frc_tmp))\n", " mod_daily=np.append(mod_daily, np.mean(e_mod_tmp[name][mode]))\n", " t_daily=np.append(t_daily,t_sim+datetime.timedelta(hours=12))\n", " else: \n", " print '{mode} simulation for {start} did not occur'.format(mode=mode, start=t_sim)\n", "\n", " # Plotting time series\n", " ax=axs[0]\n", " ax.plot(t_frc, e_frc, 'b', label = 'Forcing error', lw=2)\n", " ax.plot(t_mod, e_mod, 'g', lw=2, label = 'Model error')\n", " ax.set_title(' Comparison of {mode} error at {name}'.format(mode=mode,name=name))\n", " ax.set_ylim([-.4,.4])\n", " hfmt = mdates.DateFormatter('%m/%d %H:%M')\n", " \n", " # Plotting daily means \n", " ax=axs[1]\n", " ax.plot(t_daily, frc_daily, 'b', label = 'Forcing daily mean error', lw=2)\n", " ax.plot([t_frc[0],t_frc[-1]],[np.mean(e_frc),np.mean(e_frc)], '--b', label='Mean forcing error', lw=2)\n", " ax.plot(t_daily, mod_daily, 'g', lw=2, label = 'Model daily mean error')\n", " ax.plot([t_mod[0],t_mod[-1]],[np.mean(e_mod),np.mean(e_mod)], '--g', label='Mean model error', lw=2)\n", " ax.set_title(' Comparison of {mode} daily mean error at {name}'.format(mode=mode,name=name))\n", " ax.set_ylim([-.2,.2])\n", "\n", " # Plot tides\n", " ax=axs[2]\n", " ax.plot(ttide.time,ttide.pred_all, 'k', lw=2, label='tides')\n", " ax.set_title('Tidal predictions')\n", " ax.set_ylim([-3,3])\n", " \n", " # format axes\n", " hfmt = mdates.DateFormatter('%m/%d %H:%M')\n", " for ax in axs:\n", " ax.xaxis.set_major_formatter(hfmt)\n", " ax.legend(loc=2, ncol=4)\n", " ax.grid()\n", " ax.set_xlim([start,end+datetime.timedelta(days=1)])\n", " ax.set_ylabel('[m]')\n", " \n", " return fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Clear tidal signal in model errors. I don't think we are removing the tidal energy in the residual calculation. \n", "* Bizarre forcing behavior on Jan 22. Looked at the ssh text file in run directory and everything was recorded as a forecast. Weird!! \n", "\n", "Is it possible that this text file did not generate the forcing for the Jan 22 nowcast run?\n", "* Everything produced by Jan 22 (18hr) text file is a fcst\n", "* worker links forcing in obs and fcst. So the obs/Jan21 was not related to this text file. But does that matter? This is a nowcast so it should only use Jan 22 forcing data fcst.\n", "\n", "There are 4 Jan 22 ssh text files in /ocean/nsoontie/MEOPAR/sshNeahBay/txt/\n", "* ssh-2015-02-22_12.txt is a forecast2 file\n", "* '' 18, 19, 21 are all in forecast/22jan15\n", "* '' 18 are is also in nowcast/22jan15\n", "\n", "So it appears that the forecast had to be restarted several times. What about the nowcast? Did that run smoothly?\n", "\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_filenames(t_orig, t_final, period, grid, model_path):\n", " \"\"\"Returns a list with the filenames for all files over the\n", " defined period of time and sorted in chronological order.\n", "\n", " :arg t_orig: The beginning of the date range of interest.\n", " :type t_orig: datetime object\n", "\n", " :arg t_final: The end of the date range of interest.\n", " :type t_final: datetime object\n", "\n", " :arg period: Time interval of model results (eg. 1h or 1d).\n", " :type period: string\n", "\n", " :arg grid: Type of model results (eg. grid_T, grid_U, etc).\n", " :type grid: string\n", "\n", " :arg model_path: Defines the path used (eg. nowcast)\n", " :type model_path: string\n", "\n", " :returns: files, a list of filenames\n", " \"\"\"\n", "\n", " numdays = (t_final-t_orig).days\n", " dates = [t_orig + datetime.timedelta(days=num)\n", " for num in range(0, numdays+1)]\n", " dates.sort()\n", "\n", " allfiles = glob.glob(model_path+'*/SalishSea_'+period+'*_'+grid+'.nc')\n", " sdt = dates[0].strftime('%Y%m%d')\n", " edt = dates[-1].strftime('%Y%m%d')\n", " sstr = 'SalishSea_{}_{}_{}_{}.nc'.format(period, sdt, sdt, grid)\n", " estr = 'SalishSea_{}_{}_{}_{}.nc'.format(period, edt, edt, grid)\n", "\n", " files = []\n", " for filename in allfiles:\n", " if os.path.basename(filename) >= sstr:\n", " if os.path.basename(filename) <= estr:\n", " files.append(filename)\n", "\n", " files.sort(key=os.path.basename)\n", "\n", " return files" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def combine_files(files, var, depth, j, i):\n", " \"\"\"Returns the value of the variable entered over\n", " multiple files covering a certain period of time.\n", "\n", " :arg files: Multiple result files in chronological order.\n", " :type files: list\n", "\n", " :arg var: Name of variable (sossheig = sea surface height,\n", " vosaline = salinity, votemper = temperature,\n", " vozocrtx = Velocity U-component,\n", " vomecrty = Velocity V-component).\n", " :type var: string\n", "\n", " :arg depth: Depth of model results ('None' if var=sossheig).\n", " :type depth: integer or string\n", "\n", " :arg j: Latitude (y) index of location (<=897).\n", " :type j: integer\n", "\n", " :arg i: Longitude (x) index of location (<=397).\n", " :type i: integer\n", "\n", " :returns: var_ary, time - array of model results and time.\n", " \"\"\"\n", "\n", " time = np.array([])\n", " var_ary = np.array([])\n", "\n", " for f in files:\n", " G = nc.Dataset(f)\n", " if depth == 'None':\n", " var_tmp = G.variables[var][:, j, i]\n", " else:\n", " var_tmp = G.variables[var][:, depth, j, i]\n", "\n", " var_ary = np.append(var_ary, var_tmp, axis=0)\n", " t = nc_tools.timestamp(G, np.arange(var_tmp.shape[0]))\n", " for ind in range(len(t)):\n", " t[ind] = t[ind].datetime\n", " time = np.append(time, t)\n", "\n", " return var_ary, time" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_files(ax, grid_B, files, var, depth, t_orig, t_final,\n", " name, label, colour):\n", " \"\"\"Plots values of variable over multiple files covering\n", " a certain period of time.\n", "\n", " :arg ax: The axis where the variable is plotted.\n", " :type ax: axis object\n", "\n", " :arg grid_B: Bathymetry dataset for the SalishSeaCast NEMO model.\n", " :type grid_B: :class:`netCDF4.Dataset`\n", "\n", " :arg files: Multiple result files in chronological order.\n", " :type files: list\n", "\n", " :arg var: Name of variable (sossheig = sea surface height,\n", " vosaline = salinity, votemper = temperature,\n", " vozocrtx = Velocity U-component,\n", " vomecrty = Velocity V-component).\n", " :type var: string\n", "\n", " :arg depth: Depth of model results ('None' if var=sossheig).\n", " :type depth: integer or string\n", "\n", " :arg t_orig: The beginning of the date range of interest.\n", " :type t_orig: datetime object\n", "\n", " :arg t_final: The end of the date range of interest.\n", " :type t_final: datetime object\n", "\n", " :arg name: The name of the station.\n", " :type name: string\n", "\n", " :arg label: Label for plot line.\n", " :type label: string\n", "\n", " :arg colour: Colour of plot lines.\n", " :type colour: string\n", "\n", " :returns: axis object (ax).\n", " \"\"\"\n", "\n", " bathy, X, Y = tidetools.get_bathy_data(grid_B)\n", " lat = figures.SITES[name]['lat']; lon = figures.SITES[name]['lon']\n", " [j, i] = tidetools.find_closest_model_point(lon, lat, X, Y,\n", " bathy, allow_land=False)\n", "\n", " # Call function\n", " var_ary, time = combine_files(files, var, depth, j, i)\n", "\n", " # Plot\n", " ax.plot(time, var_ary, label=label, color=colour, linewidth=2)\n", "\n", " # Figure format\n", " ax_start = t_orig\n", " ax_end = t_final + datetime.timedelta(days=1)\n", " ax.set_xlim(ax_start, ax_end)\n", " hfmt = mdates.DateFormatter('%m/%d %H:%M')\n", " ax.xaxis.set_major_formatter(hfmt)\n", "\n", " return ax" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def compare_ssh_tides(grid_B, files, t_orig, t_final, name, PST=0, MSL=0,\n", " figsize=(20, 5)):\n", " \"\"\"\n", " :arg grid_B: Bathymetry dataset for the SalishSeaCast NEMO model.\n", " :type grid_B: :class:`netCDF4.Dataset`\n", "\n", " :arg files: Multiple result files in chronological order.\n", " :type files: list\n", "\n", " :arg t_orig: The beginning of the date range of interest.\n", " :type t_orig: datetime object\n", "\n", " :arg t_final: The end of the date range of interest.\n", " :type t_final: datetime object\n", "\n", " :arg name: Name of station.\n", " :type name: string\n", "\n", " :arg PST: Specifies if plot should be presented in PST.\n", " 1 = plot in PST, 0 = plot in UTC.\n", " :type PST: 0 or 1\n", "\n", " :arg MSL: Specifies if the plot should be centred about mean sea level.\n", " 1=centre about MSL, 0=centre about 0.\n", " :type MSL: 0 or 1\n", "\n", " :arg figsize: Figure size (width, height) in inches.\n", " :type figsize: 2-tuple\n", "\n", " :returns: matplotlib figure object instance (fig).\n", " \"\"\"\n", "\n", " # Figure\n", " fig, ax = plt.subplots(1, 1, figsize=figsize)\n", "\n", " # Model\n", " ax = plot_files(ax, grid_B, files, 'sossheig', 'None',\n", " t_orig, t_final, name, 'Model', colours['model'])\n", " # Tides\n", " figures.plot_tides(ax, name, PST, MSL, color=colours['predicted'])\n", "\n", " # Figure format\n", " ax.set_title('Modelled Sea Surface Height versus Predicted Tides at {station}: {t_start:%d-%b-%Y} to {t_end:%d-%b-%Y}'.format(station=name, t_start=t_orig, t_end=t_final))\n", " ax.set_ylim([-3.0, 3.0])\n", " ax.set_xlabel('[hrs]')\n", " ax.legend(loc=2, ncol=2)\n", " ax.grid()\n", "\n", " return fig" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_wlev_residual_NOAA(t_orig, elements, figsize=(20, 5)):\n", " \"\"\" Plots the water level residual as calculated by the function\n", " calculate_residual_obsNB and has the option to also plot the\n", " observed water levels and predicted tides over the course of one day.\n", "\n", " :arg t_orig: The beginning of the date range of interest.\n", " :type t_orig: datetime object\n", "\n", " :arg elements: Elements included in figure.\n", " 'residual' for residual only and 'all' for residual,\n", " observed water level, and predicted tides.\n", " :type elements: string\n", "\n", " :arg figsize: Figure size (width, height) in inches.\n", " :type figsize: 2-tuple\n", "\n", " :returns: fig\n", " \"\"\"\n", "\n", " res_obs_NB, obs, tides = calculate_residual_obsNB('Neah Bay', t_orig)\n", "\n", " # Figure\n", " fig, ax = plt.subplots(1, 1, figsize=figsize)\n", "\n", " # Plot\n", " ax.plot(obs.time, res_obs_NB, 'Gray', label='Obs Residual', linewidth=2)\n", " if elements == 'all':\n", " ax.plot(obs.time, obs.wlev,\n", " 'DodgerBlue', label='Obs Water Level', linewidth=2)\n", " ax.plot(tides.time, tides.pred[tides.time == obs.time],\n", " 'ForestGreen', label='Pred Tides', linewidth=2)\n", " if elements == 'residual':\n", " pass\n", " ax.set_title('Residual of the observed water levels at Neah Bay: {t:%d-%b-%Y}'.format(t=t_orig))\n", " ax.set_ylim([-3.0, 3.0])\n", " ax.set_xlabel('[hrs]')\n", " hfmt = mdates.DateFormatter('%m/%d %H:%M')\n", " ax.xaxis.set_major_formatter(hfmt)\n", " ax.legend(loc=2, ncol=3)\n", " ax.grid()\n", " \n", " return fig" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def feet_to_metres(feet):\n", " \"\"\" Converts feet to metres.\n", " \n", " :returns: metres\n", " \"\"\"\n", " \n", " metres = feet*0.3048\n", " return metres" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def load_surge_data(filename_NB):\n", " \"\"\"Loads the textfile with surge predictions for Neah Bay.\n", "\n", " :arg filename_NB: Path to file of predicted water levels at Neah Bay.\n", " :type filename_NB: string\n", "\n", " :returns: data (data structure)\n", " \"\"\"\n", "\n", " # Loading the data from that text file.\n", " data = pd.read_csv(filename_NB, skiprows=3,\n", " names=['date', 'surge', 'tide', 'obs',\n", " 'fcst', 'anom', 'comment'], comment='#')\n", " # Drop rows with all Nans\n", " data = data.dropna(how='all')\n", "\n", " return data" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def to_datetime(datestr, year, isDec, isJan):\n", " \"\"\" Converts the string given by datestr to a datetime object.\n", " The year is an argument because the datestr in the NOAA data\n", " doesn't have a year. Times are in UTC/GMT.\n", "\n", " :arg datestr: Date of data.\n", " :type datestr: datetime object\n", "\n", " :arg year: Year of data.\n", " :type year: datetime object\n", "\n", " :arg isDec: True if run date was December.\n", " :type isDec: Boolean\n", "\n", " :arg isJan: True if run date was January.\n", " :type isJan: Boolean\n", "\n", " :returns: dt (datetime representation of datestr)\n", " \"\"\"\n", "\n", " dt = datetime.datetime.strptime(datestr, '%m/%d %HZ')\n", " # Dealing with year changes.\n", " if isDec and dt.month == 1:\n", " dt = dt.replace(year=year+1)\n", " elif isJan and dt.month == 12:\n", " dt = dt.replace(year=year-1)\n", " else:\n", " dt = dt.replace(year=year)\n", " dt = dt.replace(tzinfo=tz.tzutc())\n", "\n", " return dt" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def retrieve_surge(data, run_date):\n", " \"\"\" Gathers the surge information a forcing file from on run_date.\n", "\n", " :arg data: Surge predictions data.\n", " :type data: data structure\n", "\n", " :arg run_date: Simulation run date.\n", " :type run_date: datetime object\n", "\n", " :returns: surges (meteres), times (array with time_counter)\n", " \"\"\"\n", "\n", " surge = []\n", " times = []\n", " isDec, isJan = False, False\n", " if run_date.month == 1:\n", " isJan = True\n", " if run_date.month == 12:\n", " isDec = True\n", " # Convert datetime to string for comparing with times in data\n", " for d in data.date:\n", "\n", " dt = _to_datetime(d, run_date.year, isDec, isJan)\n", " times.append(dt)\n", " daystr = dt.strftime('%m/%d %HZ')\n", " tide = data.tide[data.date == daystr].item()\n", " obs = data.obs[data.date == daystr].item()\n", " fcst = data.fcst[data.date == daystr].item()\n", " if obs == 99.90:\n", " # Fall daylight savings\n", " if fcst == 99.90:\n", " # If surge is empty, just append 0\n", " if not surge:\n", " surge.append(0)\n", " else:\n", " # Otherwise append previous value\n", " surge.append(surge[-1])\n", " else:\n", " surge.append(_feet_to_metres(fcst-tide))\n", " else:\n", " surge.append(_feet_to_metres(obs-tide))\n", "\n", " return surge, times" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Close up" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def compare_errors1(name, mode, start, end, grid_B, figsize=(20,3)):\n", " \"\"\" compares the model and forcing error at a station between dates start and end\n", " for a simulation mode.\"\"\"\n", " \n", " # array of dates for iteration\n", " numdays = (end-start).days\n", " dates = [start + datetime.timedelta(days=num)\n", " for num in range(0, numdays+1)]\n", " dates.sort()\n", " \n", " # intiialize figure and arrays\n", " fig,ax = plt.subplots(1,1,figsize=figsize)\n", " e_frc=np.array([])\n", " t_frc=np.array([])\n", " e_mod=np.array([]) \n", " t_mod=np.array([])\n", " \n", " ttide=figures.get_tides(name)\n", " \n", " for t_sim in dates:\n", " # check if the run happened\n", " if mode in verified_runs(t_sim):\n", " # retrieve forcing and model error\n", " e_frc_tmp, t_frc_tmp = calculate_error_forcing('Neah Bay', [mode], t_sim)\n", " e_mod_tmp, t_mod_tmp, _ = calculate_error_model([name], [mode], grid_B, t_sim)\n", " e_frc_tmp= figures.interp_to_model_time(t_mod_tmp[name][mode],e_frc_tmp['Neah Bay'][mode],t_frc_tmp['Neah Bay'][mode])\n", " # append to larger array\n", " e_frc = np.append(e_frc,e_frc_tmp)\n", " t_frc = np.append(t_frc,t_mod_tmp[name][mode])\n", " e_mod = np.append(e_mod,e_mod_tmp[name][mode])\n", " t_mod = np.append(t_mod,t_mod_tmp[name][mode])\n", " else: \n", " print '{mode} simulation for {start} did not occur'.format(mode=mode, start=t_sim)\n", "\n", " # Plotting time series\n", " ax.plot(t_mod, e_mod*5, 'g', lw=2, label = 'Model error x 5')\n", " ax.plot(ttide.time,ttide.pred_all, 'k', lw=2, label='tides')\n", " ax.set_title(' Comparison of {mode} error at {name}'.format(mode=mode,name=name))\n", " ax.set_ylim([-3,3])\n", " hfmt = mdates.DateFormatter('%m/%d %H:%M')\n", " ax.xaxis.set_major_formatter(hfmt)\n", " ax.legend(loc=2, ncol=4)\n", " ax.grid()\n", " ax.set_xlim([start,end+datetime.timedelta(days=1)])\n", " ax.set_ylabel('[m]')\n", " \n", " return fig" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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de5P4uTeJn/uS2Lk3iZ/7ktgJR5MElRsIDw+HTqeD0WjMcd2lS5eiffv2BdAqUdAiIyNR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\n2ekb89M35qdfzE7fmJ9+MTtyN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p+w4fPoyYmBhDJRBIPtBTJyEhIUP7yOj2qTl8+DAiIyMRGRmJ2bNnO2SfRiEFyoUQQggh\nhBBC5ASSoHKCbt26YfHixXp50aJF6NGjR7JEw507d9CjRw+UKFECFSpUwJdffqnXm81mDB06FMWL\nF0elSpWwbt26ZPu/c+cO+vTpg4CAAJQpUwaff/65XcP/AGDPnj1o1KgR/Pz8UKdOHWzfvl2va9as\nGT777DM0btwY+fPnx9mzZ+Hl5YVvv/0WlStXRtWqVQEAc+bMQeXKlVG0aFG0b98eoaGheh8pbZ/U\nihUrULFiRURGRgIANmzYAH9//wd6nCVl73PLbrp3746QkBC0bdsWBQoUwFdffZVsqOe5c+fwzDPP\noGDBgmjZsiXCwsKSPT6tWC9cuBCVKlVCwYIFUbFiRQQGBrr0uQnnknoAxiWxMzaJn7FJ/IxLYmds\nEj/jktgJR8vl7gY4mhrluF5KHJm5nisNGzbEkiVLcPz4cVSuXBkrVqzArl278Nlnn+ltBg0ahMjI\nSJw7dw5hYWFo2bIl/P398eabb2L27NlYt24dgoOD8dBDD6Fjx47Jel/16tULpUqVwpkzZxAVFYU2\nbdqgbNmy6Q5/u3z5Mtq0aYOlS5eiVatW+OOPP9CpUyecOHECRYsWBQAsXboUGzZsQNWqVZGYmAgA\nWLNmDf7++2/kzZsXW7ZswSeffIJNmzahRo0aGDp0KLp06ZIs+ZF0+/t17twZa9euxeDBgzFx4kT0\n7dsX8+bN08dPydNPPw2z2YxGjRph8uTJKF++vH2BcABH9nrLaE+oJUuWYOfOnZg3bx6effZZnD9/\nHsOHD9fr33jjDTRu3Bh//PEH9uzZg9atW6NDhw4A0o51njx58N577+Gff/5B5cqVce3atTQThEII\nIYQQQgghhLNJDyon6d69OxYvXqwTOaVLl9brEhMTsWLFCowbNw758uVD+fLlMWTIECxZsgQAsHLl\nSnzwwQcoXbo0/Pz88Mknn+jkxrVr17BhwwZMmTIFefPmRfHixfH+++9j+fLl6bZp6dKleOmll9Cq\nVSsAQIsWLVC/fn3dQ0sphV69eqF69erw8vKCj48PAGDEiBEoXLgwcufOje+//x59+vRBnTp14Ovr\ni3HjxmH37t3Jamsl3T4lM2bMwJYtW9C8eXO0a9cOL730Uqpt/vPPP3HhwgUcP34cAQEBaNOmjU6c\n5WQhISH4559/MGbMGPj4+KBp06Zo27atXp9WrJVS8PLy0kNPS5YsiRo1arjrqQgnkHoAxiWxMzaJ\nn7FJ/IxLYmdsEj/jktgJR8t2Pagy2+vJkZRS6N69O5o2bYpz5849MLwvLCwM8fHxyXoClStXDpcv\nXwYAhIaGomzZssnW2Vy4cAHx8fHw9/fX95nN5mTbpObChQv44YcfsHbtWn1fQkICnn32Wb2c9Lgp\n3RcaGor69evr5Xz58qFo0aK4fPmybkNK+0iqUKFCeOWVVzBlyhSsXr06zW2bNGmiH/P111+jUKFC\nOH78OGrWrJnm4xzFU+s/XblyBX5+fsl6qZUvXx4XL14EkHasH3roIaxYsQITJ05Enz590LhxY0ya\nNCnFIZlCCCGEEEIIIYQrSA8qJylXrhwqVqyIDRs2oGPHjsnWFStWDD4+Pjh//ry+LyQkBGXKlAEA\n+Pv7J+uRlPTvsmXL6lkCw8PDER4ejjt37uDw4cN2tal79+76ceHh4YiMjMRHH32kt0lpSFvS+wIC\nApK1Ozo6Gjdv3kzWQyy9YXHBwcFYsGAB3njjDQwaNCjddtvYkkWemjRyhtTOpb+/P8LDw3H37l19\n34ULF/T26cW6ZcuW2LhxI65evYpq1aqhX79+zn8ywmWkHoBxSeyMTeJnbBI/45LYGZvEz7gkdsLR\nJEHlRPPmzcOWLVseqMXk7e2N1157DZ9++imioqJw4cIFTJkyBd26dQMAvPbaa5g2bRouX76M8PBw\njB8/Xj/W398fLVu2xIcffojIyEiYzWacOXMGf/75Z7rt6datG9auXYuNGzciMTERsbGx2LZtm+65\nBaSf/Hn99dexYMECHDx4EHFxcfjkk0/QsGFDu3pwAUBsbCy6deuGcePGYf78+bh8+TJmzpyZ4rbH\njh1DcHAwEhMTERUVhQ8//BBlypRB9erV7TpWdlCyZEmcOXPmgfvLly+P+vXrY+TIkYiPj8fOnTvx\n66+/6vVpxfr69etYs2YNoqOj4ePjg3z58sHb29uVT0sIIYQQQgghhEhGElROVLFiRTz++ON6OWlv\nmOnTpyNfvnyoWLEimjZtiq5du6J3794AgH79+uGFF15A7dq1Ub9+fXTq1CnZYxcvXox79+6hRo0a\nKFKkCF599VVcvXpVHyO1XjdlypTBmjVrMHbsWJQoUQLlypXDpEmTkiWl7n/s/cvPPfccxowZg06d\nOiEgIADnzp1LVv8qvd5TI0aMQPny5fH222/D19cXS5cuxWeffZZiEubatWvo0qULChUqhEqVKuHi\nxYv49ddfc1QyZcSIEfjiiy9QpEgRrFq1Ktn5DQwMRFBQEIoUKYLRo0ejZ8+eel1asTabzZgyZQpK\nly6NokWLYseOHakmCYUxST0A45LYGZvEz9gkfsYlsTM2iZ9xSeyEoylPHi6llGJK7VNK5ahhXkK4\nk7zehBBCCCGEECJ7sX7Pc9y09Q4gPaiEECKbkXoAxiWxMzaJn7FJ/IxLYmdsEj/jktgJR3Nbgkop\nVVYptVUpdVQpdUQpNdhdbRFCCCGEEEIIIYQQ7uO2IX5KqVIASpEMVkrlB7APQAeS/ybZRob4CeFm\n8noTQgghhBBCiOxFhvglQfIqyWDr31EA/gUQ4K72CCGEEEIIIYQQQgj38IgaVEqpCgDqAghyb0uE\nEML4pB6AcUnsjE3iZ2wSP+OS2BmbxM+4JHbC0XK5uwHW4X0/AnjP2pMqmV69eqFChQoAgMKFC6NO\nnTqubaAQAsB/H0C26WRlWZZl2fHLNp7SHlnO2LKNp7RHljO2bOMp7ZFl+5eDg4M9qj2yLPHLKcvB\nwcEe1R5ZTnt56tSpCA4O1vkVT+S2GlQAoJTyAfArgA0kp6awPtUaVEII15EaVEIIIYQQQgiRfXhi\nDSp3FklXABYBuEnyg1S2STFBJYQQQgghhBBCCCEyxxMTVF5uPHZjAN0ANFdKHbDeWrmxPcLBbF0K\nhfFI7IxN4mdcEjtjk/gZm8TPuCR2xibxMy6JnXA0t9WgIrkTHlKkXQghhBBCCCGEEEK4j1trUKVH\nhvgJIYQQQgghhBBCOJYM8RNCCCGEEEIIIYQQ4j6SoBJOI2OSjUtiZ2wSP+OS2BmbxM/YJH7GJbEz\nNomfcUnshKNJgkoIIYQQQgghhBBCuJXUoBJCCCGEEEIIIYTIQaQGlRBCCCGEEEIIIYQQ95EElXAa\nGZNsXBI7Y5P4GZfEztgkfsYm8TMuiZ2xSfyMS2InHE0SVEIIIYQQQgghhBDCraQGlRBCCCGEEEII\nIUQOIjWohBBCCCGEEEIIIYS4jySohNPImGTjktgZm8TPuCR2xibxMzaJn3FJ7IxN4mdcEjvhaJKg\nEkIIIYQQQgghhBBuJTWohBBCCCGEEEIIIXIQqUElhBBCCCGEEEIIIcR9JEElnEbGJBuXxM7YJH7G\nJbEzNomfsUn8jEtiZ2wSP+OS2AlHkwSVEEIIIYQQQgghhHArqUElhBBCCCGEEEIIkYNIDSohhBBC\nCCGEEEIIIe4jCSrhNDIm2bgkdsYm8TMuiZ2xSfyMTeJnXBI7Y5P4GZfETjiaWxNUSqn5SqlrSqnD\n7myHEEIIIYQQQgghhHAft9agUko1BRAFYDHJR1NYLzWohBBCCCGEEEIIIRxIalDdh+QOAOHubIMQ\nQgghhBBCCCGEcC+pQSWcRsYkG5fEztgkfsYlsTM2iZ+xSfyMS2JnbBI/54mLi0NUVJTT9i+xc66D\nBw9i4cKFGDFiBDp16oRatWrB398frVu3xvjx47Fr1y7ExcW5u5kOlcvdDUhPr169UKFCBQBA4cKF\nUadOHTRr1gzAfy8IWfbM5eDgYI9qjyzLsizLsqcv23hKe2Q5Y8s2ntIeWc7Yso2ntEeW7V8ODg72\nqPbIssTPXcuBgYE4evQoIiMjERQUhP379yMhIQHVqlVDgwYNULhwYVSvXh39+vWDt7d3lo8n3/ec\ns1yrVi289957CAwMRErWr1+P9evXAwB8fHzQsWNHzJkzB/v27Utz/1OnTkVwcLDOr3git9agAgCl\nVAUAa6UGlRBCCCGEEEK4h9lsxv79+xEXF4fSpUsjICAAvr6+7m6WsMPNmzfRv39/rFy5Mtn9Sink\nypUL8fHxye5//PHHsXz5clSuXNmVzRTpIIkff/wRAwYMwI0bN5A3b160b98e1apVQ9WqVVGlShUU\nLlwYe/bswY4dO7Bjxw4cPXoUgCWm69evR8mSJe0+nifWoJIElRBCCCGEEELkQCQRFBSElStX4ocf\nfsClS5eSrS9RogTKly+Pd999Fz179oSXl5ebWipSs2HDBvTp0wehoaHIly8fWrRogQYNGuDJJ59E\n/fr1kSdPHhw6dAh79+7F3r17sWnTJoSGhiJ//vyYOXMmunXr5u6nIABcvXoVAwYMwOrVqwFYej3N\nnTsXlSpVSvNxp06dwosvvogzZ86gYsWK+O233+xOPHpiggok3XYDsAzAFQBxAC4C6H3fegrj2rp1\nq7ubIDJJYmdsEj/jktgZm8TP2CR+WWM2mxkWFsZjx47x+PHjNJvNLju2xC7jEhISOGnSJJYrV44A\n9K1MmTJs0KABS5cuTS8vr2TrGjRowD179ji8LRK/zImKiuI777yj49OkSROeOXMm3cfdvn2bnTt3\n1o/r0aMHIyMjM9UGiZ1jnDt3jqVLlyYAFihQgLNmzWJiYqLdj7927Rrr1atHACxevDj37t1r1+Os\n+Ra35oTuv7k1BU7ydZIBJHOTLEtygTvbI4QQQgghhEhffHw85s+fj0aNGqF06dLw9fVFsWLFUKNG\nDVSrVg316tXDd999h8jISHc3Vdzn3LlzaN68OYYMGYKQkBCUKVMG77//Pv766y9cuHABQUFBuHTp\nEu7du4fLly9j0aJF8Pf3x969e9GwYUP07t0bV69edffTyNHOnDmDunXrYtasWfDx8cGECROwbds2\nVKxYMd3HFipUCMuWLcPcuXORN29eLF68GPXq1cP58+ed33DxgOvXr6Nly5a4fPkynnrqKRw5cgRv\nv/12hnorlihRAtu2bUPLli1x48YNNG/eXNefMhq3D/FLiwzxE0IIIYTIvqKjo7F+/XrcunULVapU\nQdWqVeHv7w+lPGvEgfhPXFwcFixYgPHjx+PChQvJ1hUuXBglSpRAWFgYbt26BQDInz8/unbtivff\nfx/VqlVzR5OFFUksXLgQgwcPRlRUFEqWLInvvvsObdu2TffLcGRkJMaOHYvJkyfj3r178PPzw/bt\n2/Hoow9UaRFOduTIETz//PO4evUqatWqhe+//x6PPfZYpvZ17NgxdOnSBYcPH0aTJk2wbds2eHt7\nO7jFIjURERFo3rw59u/fjzp16mDbtm0oVKhQpvd379499O3bF0uWLMHDDz+Mf//9F7lz5051exni\nl/EhgHZ1TRNCCCFEzhUfH8+wsDB3N0PYKT4+nuvXr2e3bt2YL1++ZEOIADB//vysV68eZ8yYwYSE\nBHc3V1glJiZy5syZehgKAFavXp1LlixhSEgIY2Nj9bYxMTH8/vvv2bRpU71t3rx5uXbtWjc+g5wt\nLCyMHTp00PHo1KkTb9y4keH9nDx5ks8++ywBsGzZsrx8+bITWitSExQUxCJFihAAn332WUZERGR5\nn2FhYSxVqhQBcNKkSQ5opXOdCz/Hfr/0Y3BosLubkiUxMTFs3rw5AbBSpUq8evWqQ/YbHx/P6tWr\nEwCnTp2a5rbwwCF+bm9Amo2TBJWhyZhk45LYGZvEzz7x8fEurZFiD4ld+sxmM7du3crx48eza9eu\nrF27Nn19fQmAdevW5cSJE3np0iW3tE3il7aEhAROnjyZJUqUSJaQevLJJ9mzZ082atSIRYsWTbau\nYcOGPHTokEvaJ/FLXUhIiE5KAOCjjz7KFStW2JVAPHr0KLt06UIA9PLy4qxZsxzePold2v7++2+W\nL1+eAFi2VSNsAAAgAElEQVSwYEEuXrw4S59/MTExbNSokX7fzWz9IhuJn322bNnC/PnzEwDbtm3L\nmJgYh+37l19+IQDmyZOH//77r92Pc3XsrkZeZaWvKxEm8LlFz7n02I4UHx/Pl19+mQBYqlQpu2qH\nZcSaNWsIgEWLFuXt27dT3U4SVJKgylHkw8a4JHZpi42N5dq1azlkyBBOmDCBa9as4YkTJxgfH+/u\nppGU+KVn8+bNfOKJJ/SXpYceeohFihRh6dKl+corr3DXrl1uS1xJ7NJ25coVtm/f/oEeNwCYO3du\n/bdSis2bN2dgYKAUavYQx48f119oAbBq1aocPXo0T58+/cC2YWFhXL58OQMCAgiAuXLl4ogRI3j3\n7l2ntlHi9yCz2cylS5eyUKFCuvjuihUrMlS817af//u//9Px//TTTx362pTYpW7OnDk6id+gQQOe\nP3/eIfu9ceMGH3nkEQJg69ats3QNJPFL3+rVq/XnXNeuXXnv3j2HH6Nnz576RwN74+nK2N2Ouc06\ns+oQJujb2VtnXXZ8R4mNjdVF6gsVKsSDBw86/Bhms5lNmjTR77epkQSVJKiyLDY2lqGhoTx58iT3\n7dvH7du3c+vWrTK0wQBu377NwMBAjho1it26dWPDhg1ZrFgxlilThu3ateOoUaO4du1aXrlyxd1N\nFSmIjY3lL7/8wu7du7NgwYIpfkH28fFh/fr1uWjRIqdcOIisOXToEF988cUUY3f/7cknn+SKFSs8\nJumY05nNZi5ZsoR+fn56hpuBAwdy9uzZ3L17N+/cucOYmBiuWrWKnTp1Spas6tixI2/evOnup5Bj\nJSQkcOLEicyTJw8B0N/fn7/88otdyYnbt29zwIABVEoRAB955BEeP37cBa0WJHnz5k2+9tpr+rXU\ntm3bLA9BmTNnDr29vfXMYXFxcQ5qrbhfTEwM+/Tpo+P37rvvJhuG6QgnT57UvR779+/vcb2Ss4OI\niAj269dPx/Gdd97JcILYXuHh4XoI77hx45xyjMy6e+8un17wNGECK0+rzNbftyZM4OdbPnd30zLk\n1q1bfPrpp/W1zI4dO5x2rL/++ksPr05tKK4kqCRBlWknT57k22+/rS/wUro9/PDDfO211/jVV1+5\nrDu8sM/27dtZpkwZu74YA2DLli35zz//uLvZwurIkSMsW7ZsshjVrl2bn332Gd977z2+8MILD0zT\nXL58eU6fPt3pv/iL9N28eZO9e/fWX3ILFCjAsWPHMjo6mvHx8YyMjOSNGzd48uRJfvrpp7q2AwCW\nK1eO69atc/dTyNGuXLnCdu3a6Zi88MILDAkJSfMx4eHh/Pbbb3UyuXTp0vILvRucOXOGTz31lI5d\nz549eevWrQzv56+//mKtWrV0guvEiRNOaK1I6rffftM92PLly8c5c+Y4LPmwbt06PvTQQwTA3r17\nO2SfIrndu3ezTp06esjWwoULnXasnTt36h8FpkyZ4rTj5ETbt2/nww8/TAD09fXlxIkTnZ4E/O23\n3/TxDh8+7NRj2Ss+MZ7tlrUjTGDApACeCz/HLWe3ECawzOQyTEg0Rq3C8+fP69pQAQEBDA52fg2t\njh07EgD79euX4npJUEmCKsOCgoLYqVMn/cXK1r26YsWKrF27Nps0acKGDRsyb968yb4cK6X49ttv\nZ+pC0FHky4Dll2OTyUQvLy89Tn/48OGcO3cut2/fzkuXLvHEiRMMDAzkkCFD2Lx5c33RBoCvvvqq\nW34tltj9559//tG/DlavXp1ffvklT548meK2UVFRXLBgAatWrZrs9Tpz5kwZZuQmN27cYO3atfUw\noUGDBvH69etpPiY6OpozZ85klSpVdBxHjBjhkt5UErvk9u7dy5IlS+q6KfPmzcvQa+ns2bM6QaKU\n4qeffurU3o0Sv/8EBgayQIEC+kL8119/zdL+oqOjdTHZgICAVN+Hs0LiZznPAwYM0O99jRo1SnEY\nZlYFBQXpH10XL16c5f1J7CxCQ0P1MC3bj9f79+93+nGXL1+uk2GZGUIo8Uvu4sWLHDJkiP7+V7du\nXZcmi9566y0C4OOPP55unTlXxK7vmr6ECfQb78cj146QJBPNiaz4dUXCBG44tcHpbciqAwcO0N/f\nnwBYs2bNdH9oc5Tjx4/T29ubXl5ePHbs2APrJUElCSq73bt3j506ddIfML6+vuzTp0+K/1ikpdDa\noUOHOG/ePL755pvMlSuX/nK8cOFCt3S5zekfNhcvXtRdOJVSHDFihF1fjMLCwjh06FD9a5S3tzf7\n9evHqKgoF7TaIqfHzmbnzp26B0br1q3tLkaZkJDAH3/8kfXq1dOv4YEDB7psNiqJn0XS5FSVKlUy\n3OsiMTGRY8eO1QnmZ555xulDcCV2/1m9erX+8aVZs2a8ePFipvYTHx/Pzz77TF/ov/jii04bViTx\nIyMjI9mrVy/93tepUyeH/VgWFRXFZ555RveKO3XqlEP2a5PT47d3716dmM+VKxfHjh3r1M+tOXPm\n6B5aGSnKnJKcHru4uDhOnDhRJ4V9fX05YsSILBcvz4jXX3+dANi5c+cMPzanxy88PJw///wzBw4c\nyGrVqun3T29vb3722WcuHwobERGhRw6sXr06zW2dHbtz4ecIE5jnizz8K+SvZOu+2P4FYQJfWfmK\nU9uQGrPZzJMnT3L16tX8/fffuXfvXp46dYo3b97kmTNn+P3333Pw4MF88skn6ePjo69nwsPDXdrO\nt99+mwDYvn37B9ZJgkoSVHYxm816zHiBAgU4fPjwDE/hevToUX0RB4BNmzZ1yq+NImWHDh3Sw4RK\nlSrFP/74I8P7uHjxIvv166drNTzzzDMuvdDI6TZt2qR7s7366quZujiw1c2xFSdt06aNxNBFbty4\nwccee0wnp7IyDfbWrVv19MslS5bM8RfSzmY2mzlp0iSdUOrVq5dDLs63bdvGYsWKEQBfe+01lyWM\nc5L9+/frBEeePHn43XffOfwHsqioKP3jT5kyZZzSuyenuXPnDj/88EN9vVGjRg2X9Loxm8184403\nCFhmBZQh8Rl3+vRpfvzxx7qnqa1WmKOTt/YICQnRPyo4s66O0d27d4979uzh9OnT2b1792QJKdst\nf/78bNu2Lffs2eO2dk6bNo2AZSZVd9YWm7d/HmECOyzv8MC6S3cu0WuUF31G+/B6VNq94x3l4MGD\nnDx5Mjt27PjAjLRp3ZRS7N27t8PrwNnjypUr+jtNUFBQsnWSoJIElV3Gjh1LwFLQ7P5/oowwm81c\ntGgRixcvrr9YHTlyxIEtFSmJiorSHzbPP/88r127lqX9HT58WNeBaNy4Me/cueOglorUrFu3TieV\nevbsmeWhXX/++adOWD7++ONSCN/JHJmcsgkNDWWzZs10zwJJUjlHfHw8+/fvry/ovvzyS4deGO/b\nt0/3iuzbt68U9HUQs9nMKVOm6PfNWrVqOfV6IzIyUs9OVKFChTSn0BapM5vNDAwM1MNOlFL84IMP\nHDp1fXoiIiJYuXJlAuBbb73lsuMaWVxcHFeuXMkWLVok+wJcq1Ytrl+/3q1ts83UWK9ePacV8jaq\nW7duccKECboIedKbj48PGzV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8ebOeAa127dq8cOGCu5vkcp9++ikBsEePHvo+Z8Tu4NWD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\nPtE5d/CYc3HWV3alC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Dhw5Zus6NeCA0NBTdu3fH1q1bAQCffvopvv/+e5hMJrz33ntYuXIllFIYO3Ys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\nsvyTFClQxIkjdL62bdvi5eXFunXruHTpU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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t_orig=datetime.datetime(2015,1,10)\n", "t_final = datetime.datetime(2015,1,19)\n", "mode = 'nowcast'\n", "fig = compare_errors1('Point Atkinson', mode, t_orig,t_final,bathy)\n", "fig = compare_errors1('Victoria', mode, t_orig,t_final,bathy)\n", "fig = compare_errors1('Campbell River', mode, t_orig,t_final,bathy)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def compare_errors2(ax, name, mode, start, end, grid_B, cf, cm):\n", " \"\"\" compares the model and forcing error at a station between dates start and end\n", " for a simulation mode.\"\"\"\n", " \n", " # array of dates for iteration\n", " numdays = (end-start).days\n", " dates = [start + datetime.timedelta(days=num)\n", " for num in range(0, numdays+1)]\n", " dates.sort()\n", " \n", " # intiialize figure and arrays\n", " e_frc=np.array([])\n", " t_frc=np.array([])\n", " e_mod=np.array([]) \n", " t_mod=np.array([])\n", " # mean daily error\n", " frc_daily= np.array([])\n", " mod_daily = np.array([])\n", " t_daily = np.array([])\n", " \n", " ttide=figures.get_tides(name)\n", " \n", " for t_sim in dates:\n", " # check if the run happened\n", " if mode in verified_runs(t_sim):\n", " # retrieve forcing and model error\n", " e_frc_tmp, t_frc_tmp = calculate_error_forcing('Neah Bay', [mode], t_sim)\n", " e_mod_tmp, t_mod_tmp, _ = calculate_error_model([name], [mode], grid_B, t_sim)\n", " e_frc_tmp= figures.interp_to_model_time(t_mod_tmp[name][mode],e_frc_tmp['Neah Bay'][mode],t_frc_tmp['Neah Bay'][mode])\n", " # append to larger array\n", " e_frc = np.append(e_frc,e_frc_tmp)\n", " t_frc = np.append(t_frc,t_mod_tmp[name][mode])\n", " e_mod = np.append(e_mod,e_mod_tmp[name][mode])\n", " t_mod = np.append(t_mod,t_mod_tmp[name][mode])\n", " # append daily mean error\n", " frc_daily=np.append(frc_daily, np.mean(e_frc_tmp))\n", " mod_daily=np.append(mod_daily, np.mean(e_mod_tmp[name][mode]))\n", " t_daily=np.append(t_daily,t_sim+datetime.timedelta(hours=12))\n", " else: \n", " print '{mode} simulation for {start} did not occur'.format(mode=mode, start=t_sim)\n", " \n", " # Plotting daily means \n", " ax.plot(t_daily, frc_daily, cf, label = 'Forcing, ' + mode, lw=2)\n", " ax.plot(t_daily, mod_daily, cm, lw=2, label = 'Model, ' + mode)\n", " ax.set_title(' Comparison of daily mean error at {name}'.format(mode=mode,name=name))\n", " ax.set_ylim([-.35,.35])\n", " \n", " # format axes\n", " hfmt = mdates.DateFormatter('%m/%d %H:%M')\n", " ax.xaxis.set_major_formatter(hfmt)\n", " ax.legend(loc=2, ncol=6)\n", " ax.grid()\n", " ax.set_xlim([start,end+datetime.timedelta(days=1)])\n", " ax.set_ylabel('[m]')\n", " \n", " return fig" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "forecast simulation for 2015-01-18 00:00:00 did not occur\n", "forecast simulation for 2015-01-19 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-01 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-02 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-03 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-04 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-05 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-06 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-07 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-08 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-09 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-10 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-11 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-12 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-13 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-14 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-15 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-16 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-17 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-19 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-20 00:00:00 did not occur\n", "forecast simulation for 2015-01-18 00:00:00 did not occur\n", "forecast simulation for 2015-01-19 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-01 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-02 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-03 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-04 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-05 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-06 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-07 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-08 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-09 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-10 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-11 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-12 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-13 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-14 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-15 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-16 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-17 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-19 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-20 00:00:00 did not occur\n", "forecast simulation for 2015-01-18 00:00:00 did not occur\n", "forecast simulation for 2015-01-19 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-01 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-02 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-03 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-04 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-05 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-06 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-07 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-08 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-09 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-10 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-11 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-12 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-13 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-14 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-15 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-16 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-17 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-19 00:00:00 did not occur\n", "forecast2 simulation for 2015-01-20 00:00:00 did not occur\n" ] }, { "data": { "image/png": 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Hh/P+++/Tv3//CucKCwur8mlEf/nLX7j//vtZuHAhbm5uTJo0iYyMDMD6Ajdt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\n6qIDYDfAu2fD++dDuRPae2v5zeYv0d5zCoCVc+/lrhu+jNl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ewONXgzuYjQ5WzbuPNVXv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halQn4yREcKuwF7QER/2PyT52UcPf0EW9rR9vUy3FRNTPYS3HoDnYnpdCamo5P6Wdz/EAsG/4ku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\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wsHYzh2pfjAUAAJQVLOKdGicHQydpGWgBIN+Sz6N3PMqnb/80Zr0Jq/ccHc6DdDgP0eU8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\n+wpuefwWSqtLiXBGcM+19/Dvl/87YWG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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t_orig=datetime.datetime(2015,1,1)\n", "t_final = datetime.datetime(2015,1,31)\n", "fig,axs = plt.subplots(3,1,figsize=(20,12))\n", "for name, n in zip (['Point Atkinson','Victoria','Campbell River'], np.arange(3)):\n", " fig = compare_errors2(axs[n], name, 'nowcast', t_orig,t_final,bathy,'DeepSkyBlue','YellowGreen')\n", " fig = compare_errors2(axs[n], name, 'forecast', t_orig,t_final,bathy,'DodgerBlue','OliveDrab')\n", " fig = compare_errors2(axs[n], name, 'forecast2', t_orig,t_final,bathy,'SteelBlue','DarkGreen')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def compare_errors3(name, mode, start, end, grid_B, figsize=(20,3)):\n", " \"\"\" compares the model and forcing error at a station between dates start and end\n", " for a simulation mode.\"\"\"\n", " \n", " # array of dates for iteration\n", " numdays = (end-start).days\n", " dates = [start + datetime.timedelta(days=num)\n", " for num in range(0, numdays+1)]\n", " dates.sort()\n", " \n", " fig,ax = plt.subplots(1,1,figsize=figsize)\n", " # intiialize figure and arrays\n", " e_frc=np.array([])\n", " t_frc=np.array([])\n", " e_mod=np.array([]) \n", " t_mod=np.array([])\n", " # mean daily error\n", " frc_daily= np.array([])\n", " mod_daily = np.array([])\n", " t_daily = np.array([])\n", " \n", " ttide=figures.get_tides(name)\n", " \n", " for t_sim in dates:\n", " # check if the run happened\n", " if mode in verified_runs(t_sim):\n", " # retrieve forcing and model error\n", " e_frc_tmp, t_frc_tmp = calculate_error_forcing('Neah Bay', [mode], t_sim)\n", " e_mod_tmp, t_mod_tmp, _ = calculate_error_model([name], [mode], grid_B, t_sim)\n", " e_frc_tmp= figures.interp_to_model_time(t_mod_tmp[name][mode],e_frc_tmp['Neah Bay'][mode],t_frc_tmp['Neah Bay'][mode])\n", " # append to larger array\n", " e_frc = np.append(e_frc,e_frc_tmp)\n", " t_frc = np.append(t_frc,t_mod_tmp[name][mode])\n", " e_mod = np.append(e_mod,e_mod_tmp[name][mode])\n", " t_mod = np.append(t_mod,t_mod_tmp[name][mode])\n", " # append daily mean error\n", " frc_daily=np.append(frc_daily, np.mean(e_frc_tmp))\n", " mod_daily=np.append(mod_daily, np.mean(e_mod_tmp[name][mode]))\n", " t_daily=np.append(t_daily,t_sim+datetime.timedelta(hours=12))\n", " # stdev\n", " stdev_mod = (max(np.cumsum((mod_daily-np.mean(e_mod))**2))/len(mod_daily))**0.5\n", " else: \n", " print '{mode} simulation for {start} did not occur'.format(mode=mode, start=t_sim)\n", " \n", " # Plotting daily means \n", " ax.plot(t_daily, frc_daily, 'b', label = 'Forcing, ' + mode, lw=2)\n", " ax.plot(t_daily, mod_daily, 'g', lw=2, label = 'Model, ' + mode)\n", " #ax.plot([t_frc[0],t_frc[-1]],[np.mean(e_frc),np.mean(e_frc)], '--b', label='Mean forcing error', lw=2)\n", " #ax.plot([t_mod[0],t_mod[-1]],[np.mean(e_mod),np.mean(e_mod)], '--g', label='Mean model error', lw=2)\n", " ax.set_title(' Comparison of daily mean error at {name}'.format(mode=mode,name=name))\n", " ax.set_ylim([-.35,.35])\n", " \n", " # format axes\n", " hfmt = mdates.DateFormatter('%m/%d %H:%M')\n", " ax.xaxis.set_major_formatter(hfmt)\n", " ax.legend(loc=2, ncol=6)\n", " ax.grid()\n", " ax.set_xlim([start,end+datetime.timedelta(days=1)])\n", " ax.set_ylabel('[m]')\n", " print stdev_mod\n", " \n", " return fig" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0125982966754\n", "0.0435648311803\n", "0.0388926269505\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t_orig=datetime.datetime(2015,1,22)\n", "t_final = datetime.datetime(2015,1,24)\n", "fig = compare_errors3('Victoria', 'nowcast', t_orig,t_final,bathy)\n", "fig = compare_errors3('Victoria', 'forecast', t_orig,t_final,bathy)\n", "fig = compare_errors3('Victoria', 'forecast2', t_orig,t_final,bathy)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }