diff --git a/ASFC_notebook.ipynb b/ASFC_notebook.ipynb
index 78a2c90b483667b07225615c0a5f829066dc0823..8b137891791fe96927ad78e64b0aad7bded08bdc 100644
--- a/ASFC_notebook.ipynb
+++ b/ASFC_notebook.ipynb
@@ -1,181 +1 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Introduction\n",
- "\n",
- "AirSeaFluxCode is developed to provide an easy and accessible way to calculate turbulent surface fluxes (TSFs) from a small number of bulk variables and for a viariety of bulk algorithms. \n",
- "\n",
- "By running AirSeaFluxCode you can compare different bulk algorithms and to also investigate the effect choices within the implementation of each parameterisation have on the TSFs estimates. \n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Getting started\n",
- "\n",
- "Let's first import the basic python packages we will need for reading in our input data, to perform basic statistics and plotting"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# first import all packages you might need\n",
- "%matplotlib inline\n",
- "import matplotlib.pyplot as plt\n",
- "import netCDF4 as nc\n",
- "import numpy as np\n",
- "import pandas as pd\n",
- "from tabulate import tabulate"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### AirSeaFluxCode examples\n",
- "\n",
- "AirSeaFluxCode is set up to run in its default setting with a minimum number of input variables, namely wind speed; air temperature; and sea surface temperature. Let's load the code, import some real data composed for testing it (Research vessel data) and run AirSeaFluxCode with default settings (output height 10m, cool skin/warm layer corrections turned off, bulk algorithm Smith 1988, gustiness on, saturation vapour pressure following Buck (2012), tolerance limits set for both flux estimates and height adjusted variables (['all', 0.01, 0.01, 1e-05, 1e-3, 0.1, 0.1]), number of iterations are ten, non converged points are set to missing and Monin-Obukhov stability length is calculated following the ECMWF implementation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from AirSeaFluxCode import AirSeaFluxCode"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "inDt = pd.read_csv(\"data_all.csv\")\n",
- "date = np.asarray(inDt[\"Date\"])\n",
- "lon = np.asarray(inDt[\"Longitude\"])\n",
- "lat = np.asarray(inDt[\"Latitude\"])\n",
- "spd = np.asarray(inDt[\"Wind speed\"])\n",
- "t = np.asarray(inDt[\"Air temperature\"])\n",
- "sst = np.asarray(inDt[\"SST\"])\n",
- "rh = np.asarray(inDt[\"RH\"])\n",
- "p = np.asarray(inDt[\"P\"])\n",
- "sw = np.asarray(inDt[\"Rs\"])\n",
- "hu = np.asarray(inDt[\"zu\"])\n",
- "ht = np.asarray(inDt[\"zt\"])\n",
- "hin = np.array([hu, ht, ht])\n",
- "del hu, ht, inDt\n",
- "outvar = (\"tau\", \"sensible\", \"latent\", \"u10n\", \"t10n\", \"q10n\")\n",
- "# run AirSeaFluxCode\n",
- "res = AirSeaFluxCode(spd, t, sst, \"bulk\", meth=\"UA\", lat=lat, hin=hin, hum=[\"rh\", rh], P=p, cskin=0, Rs=sw,\n",
- " tol=['all', 0.01, 0.01, 1e-05, 1e-3, 0.1, 0.1], L=\"tsrv\", out_var = outvar)\n",
- "flg = res[\"flag\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "res is the output of AirSeaFluxCode which is a dataFrame with keys: \"tau\", \"sensible\", \"latent\", \"u10n\", \"t10n\", \"q10n\". Let's plot the flux estimates."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, ax = plt.subplots(3, 1, sharex=True, sharey=False)\n",
- "fig.tight_layout()\n",
- "ax[0].plot(res[\"tau\"], \"-\", color=\"grey\", linewidth=1, alpha = 0.8)\n",
- "ax[1].plot(res[\"sensible\"], \"-\", color=\"grey\", linewidth=1, alpha = 0.8)\n",
- "ax[2].plot(res[\"latent\"], \"-\", color=\"grey\", linewidth=1, alpha = 0.8)\n",
- "ax[0].set_ylabel('tau (Nm$^{-2}$)')\n",
- "ax[1].set_ylabel('shf (Wm$^{-2}$)')\n",
- "ax[2].set_ylabel('lhf (Wm$^{-2}$)')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can save the output in a csv file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "res.to_csv(\"test_AirSeaFluxCode.csv\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "and generate some statistics which you can save in a txt file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(\"Input summary\", file=open('./stats.txt', 'a'))\n",
- "print('input file name: {}, \\n method: {}, \\n gustiness: {}, \\n cskin: {},'\n",
- " ' \\n tolerance: {}, \\n qmethod: {}, \\n L: {}'.format(\"data_all.csv\", \"UA\", \"on\",\n",
- " 0, \"all\", \"Buck2\", \"Rb\"),\n",
- " file=open('./stats.txt', 'a'))\n",
- "ttl = np.asarray([\"tau \", \"shf \", \"lhf \", \"u10n \", \"t10n \", \"q10n \"])\n",
- "header = [\"var\", \"mean\", \"median\", \"min\", \"max\", \"5%\", \"95%\"]\n",
- "n = np.shape(res)\n",
- "stats = np.copy(ttl)\n",
- "a = res.iloc[:,:-1].to_numpy(dtype=\"float64\").T\n",
- "stats = np.c_[stats, np.nanmean(a, axis=1)]\n",
- "stats = np.c_[stats, np.nanmedian(a, axis=1)]\n",
- "stats = np.c_[stats, np.nanmin(a, axis=1)]\n",
- "stats = np.c_[stats, np.nanmax(a, axis=1)]\n",
- "stats = np.c_[stats, np.nanpercentile(a, 5, axis=1)]\n",
- "stats = np.c_[stats, np.nanpercentile(a, 95, axis=1)]\n",
- "print(tabulate(stats, headers=header, tablefmt=\"github\", numalign=\"left\",\n",
- " floatfmt=(\"s\", \"2.2e\", \"2.2e\", \"2.2e\", \"2.2e\", \"2.2e\", \"2.2e\")),\n",
- " file=open('./stats.txt', 'a'))\n",
- "print('-'*79+'\\n', file=open('./stats.txt', 'a'))\n",
- "del a"
- ]
- }
- ],
- "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": "3.9.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
+