From 7f311adf8ef253f8c5b778c2b4631afbdf196a4b Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Thu, 7 Nov 2024 11:05:26 +0100 Subject: [PATCH 01/23] added adcp and ctd examples --- docs/tutorials/ADCP_data_tutorial.ipynb | 296 ++++++++++++++++ docs/tutorials/CTD_data_tutorial.ipynb | 452 ++++++++++++++++++++++++ docs/tutorials/index.md | 2 + 3 files changed, 750 insertions(+) create mode 100644 docs/tutorials/ADCP_data_tutorial.ipynb create mode 100644 docs/tutorials/CTD_data_tutorial.ipynb diff --git a/docs/tutorials/ADCP_data_tutorial.ipynb b/docs/tutorials/ADCP_data_tutorial.ipynb new file mode 100644 index 00000000..d0db60a0 --- /dev/null +++ b/docs/tutorials/ADCP_data_tutorial.ipynb @@ -0,0 +1,296 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example ADCP data handling \n", + "### Upwelling near Peru\n", + "The ocean region near Peru is known for its upwelling, causing the rise of nutrient-rich bottom water to the surface and a subsequently flourishing ecosystem. Hence, it is an important region for wildlife and for the fishery industry. \n", + "\n", + "We explored this area with an ADCP device onboard the Virtualship. It measured zonal and meridional velocities up to 500 metres depth. In this tutorial you can see how we process the data in the context of wind-induced effects on ocean circulation." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "import cartopy.crs as ccrs\n", + "import cmocean\n", + "import numpy as np\n", + "from geopy import distance" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Download ready\n" + ] + } + ], + "source": [ + "# Download data\n", + "import requests\n", + "\n", + "files = {\n", + " \"Peru_adcp.zip\":\"https://surfdrive.surf.nl/files/index.php/s/z1qAN6VP7zbbC3z/download\"}\n", + "\n", + "for filename, url in files.items():\n", + " response = requests.get(url, allow_redirects=True)\n", + "\n", + " if response.status_code == 200:\n", + " with open(filename, \"wb\") as f:\n", + " f.write(response.content)\n", + " else:\n", + " print(\"Failed to download\", url)\n", + "print('Download ready')\n", + "\n", + "#unpack the downloaded data\n", + "!unzip -q Peru_adcp.zip -d . " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Peru ADCP data')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make a plot of the ship's trajectory. To check that it runs perpendicular to the coast of Peru.\n", + "peru = xr.open_zarr('Peru_adcp.zarr').compute()\n", + "\n", + "# for convenience, define z positive upward\n", + "zu = np.unique(-peru.z, axis=1).squeeze() # convert z to 1D and positive upward\n", + "peru = peru.assign_coords({'z':('trajectory',zu)}).sortby('z')\n", + "\n", + "# plot track\n", + "ax = plt.axes(projection=ccrs.PlateCarree())\n", + "ax.scatter(peru.lon, peru.lat, transform=ccrs.PlateCarree())\n", + "ax.coastlines()\n", + "ax.gridlines(draw_labels=True)\n", + "ax.set_extent((-85,-75,-15,-5))\n", + "ax.set_title('Peru ADCP data')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "max distance: 362.62 km\n" + ] + } + ], + "source": [ + "# Calculate the distance from each measurement to the coast and add it as variable to the dataset.\n", + "peru_surf = peru.isel(trajectory=0).compute()\n", + "d = xr.zeros_like(peru_surf.lon)\n", + "lon0,lat0 = peru_surf.lon.data[-1], peru_surf.lat.data[-1]\n", + "for ob,(lon,lat) in enumerate(zip(peru_surf.lon.data,peru_surf.lat.data)):\n", + " d[ob] = distance.distance((lon,lat),(lon0,lat0)).m\n", + "peru['s'] = -d.assign_attrs({\n", + " 'long_name':'distance to coast','units':'m','positive':'shoreward'})\n", + "peru = peru.set_coords('s').sortby('s')\n", + "print(f\"max distance from coast: {abs(peru.s.min()).data/1000:.2f} km\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate and plot the velocity components parallel and perpendicular to the coast.\n", + "\n", + "# calculations\n", + "dlon = np.deg2rad(peru_surf.lon.differentiate('obs'))\n", + "dlat = np.deg2rad(peru_surf.lat.differentiate('obs'))\n", + "lat = np.deg2rad(peru_surf.lat)\n", + "alpha = np.arctan(dlat/(dlon*np.cos(lat))).mean('obs') # cruise direction angle\n", + "Ucross = np.cos(alpha)*peru.U + np.sin(alpha)*peru.V # cross-shore vel\n", + "Ulong = -np.sin(alpha)*peru.U + np.cos(alpha)*peru.V # long-shore vel\n", + "peru['Ucross'] = Ucross.assign_attrs({'long_name':'cross-shore velocity','units':'m s-1'})\n", + "peru['Ulong'] = Ulong.assign_attrs({'long_name':'longshore velocity','units':'m s-1'})\n", + "\n", + "# figure settings\n", + "fig = plt.figure(figsize=(10,6))\n", + "norm = mcolors.CenteredNorm(halfrange=0.35)\n", + "cmap = cmocean.cm.balance\n", + "landmask = xr.where(((peru.U==0) & (peru.V==0)), 1, np.nan)\n", + "S = peru.s.broadcast_like(peru.lon)\n", + "\n", + "# cross-shore velocity plot\n", + "ax1 = fig.add_subplot(121)\n", + "p = ax1.pcolormesh(S/1000, peru.z, peru.Ucross, norm=norm, cmap=cmap)\n", + "ax1.pcolormesh(S/1000, peru.z, landmask, cmap='binary_r')\n", + "ax1.set_ylabel('depth [m]')\n", + "ax1.set_xlabel('distance from coast [km]')\n", + "ax1.set_title('cross-shore velocity (positive shoreward)')\n", + "ax1.grid()\n", + "\n", + "# alongshore velocity plot\n", + "ax2 = fig.add_subplot(122, sharey=ax1)\n", + "ax2.yaxis.tick_right()\n", + "p2 = ax2.pcolormesh(S/1000, peru.z, peru.Ulong, norm=norm, cmap=cmap)\n", + "ax2.pcolormesh(S/1000, peru.z, landmask, cmap='binary_r')\n", + "ax2.set_ylabel('depth [m]', rotation=270, labelpad=15)\n", + "ax2.set_xlabel('distance from coast [km]')\n", + "ax2.set_title('longshore velocity (positive with coast on r.h.s.)')\n", + "ax2.yaxis.set_label_position('right')\n", + "ax2.grid()\n", + "fig.colorbar(p, ax=[ax1,ax2], shrink=0.7, orientation='horizontal', label=r'm s$^{-1}$')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Assuming constant density and no velocity variations in the longshore direction, the continuity equation may be written \n", + "$$\\frac{\\partial u_{cross}}{\\partial s} + \\frac{\\partial w}{\\partial z} = 0$$ \n", + "where $u_{cross}$ is the cross-shore velocity, $s$ the shoreward distance, $w$ the vertical velocity and $z$ the depth (upward positive). " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Integrate the continuity equation to find the vertical velocity, starting from the surface downward.\n", + "peru_d = peru.sortby('z', ascending=False) # sort downward\n", + "\n", + "# # compute dwdz - easy central difference method\n", + "# dwdz = -peru_d.Ucross.differentiate('s')\n", + "\n", + "# compute dwdz - central difference using interpolated values at cell edges\n", + "# slightly more accurate\n", + "landfilter = xr.where(((peru_d.U==0) & (peru_d.V==0)), 0, 1) # 0 in land, 1 in ocean\n", + "Ucrossi = peru_d.Ucross.interp(obs=peru_d.obs-0.5) # U at cell boundaries (toward ocean)\n", + "Ucrossi = Ucrossi * landfilter.data\n", + "dUcrossds = Ucrossi.diff('obs') / Ucrossi.s.diff('obs')\n", + "dwdz = -dUcrossds\n", + "\n", + "# integrate dwdz\n", + "peru['w'] = dwdz.cumulative_integrate('z')\n", + "\n", + "# plot the vertical velocity\n", + "ax = plt.axes()\n", + "S,Z = xr.broadcast(peru.s/1000, peru.z)\n", + "w = ax.pcolormesh(S, Z, peru.w.T, norm=mcolors.CenteredNorm(), cmap=cmap)\n", + "ax.pcolormesh(S, Z, landmask.T, cmap='binary_r')\n", + "plt.title('vertical velocity')\n", + "plt.ylabel('depth [m]')\n", + "plt.xlabel('shoreward distance [km]')\n", + "plt.xlim([-350,0])\n", + "plt.colorbar(w, label=r'm s$^{-1}$')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Explain what you see: the south-easterly winds near Peru create Ekman transport away from the coast (to the left of the wind direction in the SH), leading to an SSH-gradient directed off the coast. This SSH-gradient results in a pressure gradient force directed toward the coast and a longshore geostrophic current with the coast on its right hand side. Due to friction at the bottom, the coriolis force of the current decreases and gives rise to a cross-shore current that is pushed up against the topography. This explains the column of water having positive vertical velocity near the land boundary. \n", + "\n", + "There are limitations to this point of view. The longshore geostrophic current does not show up at all depths near the coast. Likely, some baroclinic effects are important as well. Besides, the vertical velocity is derived assuming incompressibility and no longshore variations in longshore flow. Especially the last statement may lead to inaccurate results. Besides, ocean models often use \"shaved cells\" near the topographic boundary of reduced thickness. This needs to be accounted for before calculating water fluxes." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ship", + "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": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/tutorials/CTD_data_tutorial.ipynb b/docs/tutorials/CTD_data_tutorial.ipynb new file mode 100644 index 00000000..b0c88b43 --- /dev/null +++ b/docs/tutorials/CTD_data_tutorial.ipynb @@ -0,0 +1,452 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example CTD data handling \n", + "### Eddy near Japan\n", + "We explored an area near Japan with an active Eddy with the CTD device onboard the Virtualship. It measured salinity, temperature and pressure up to 2000 metres depth. In this tutorial you can see how we process the data and interpolate to create crosssections." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import gsw\n", + "import cmocean" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download data\n", + "import requests\n", + "\n", + "files = {\"Japan.zip\":\"https://surfdrive.surf.nl/files/index.php/s/1EOYBwfh1y68h79/download\"}\n", + "\n", + "for filename, url in files.items():\n", + " response = requests.get(url, allow_redirects=True)\n", + "\n", + " if response.status_code == 200:\n", + " with open(filename, \"wb\") as f:\n", + " f.write(response.content)\n", + " else:\n", + " print(\"Failed to download\", url)\n", + "print('Download ready')\n", + "\n", + "#unpack the downloaded data\n", + "!unzip -q Japan.zip -d . " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we will have to read the _csv_ files. Take a look at the comments and header of the first CTD dataset by running the following cell." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0longitude154.0{'axis': 'X', 'long_name': '', 'standard_name'...
1latitude38.099998474121094{'axis': 'Y', 'long_name': '', 'standard_name'...
2start time2022-10-18T00:00:00.000000000NaN
3end time2022-10-18T01:06:20.000000000NaN
4pressure [hPa]temperature [degC]salinity [g kg-1]
\n", + "
" + ], + "text/plain": [ + " 0 1 \\\n", + "0 longitude 154.0 \n", + "1 latitude 38.099998474121094 \n", + "2 start time 2022-10-18T00:00:00.000000000 \n", + "3 end time 2022-10-18T01:06:20.000000000 \n", + "4 pressure [hPa] temperature [degC] \n", + "\n", + " 2 \n", + "0 {'axis': 'X', 'long_name': '', 'standard_name'... \n", + "1 {'axis': 'Y', 'long_name': '', 'standard_name'... \n", + "2 NaN \n", + "3 NaN \n", + "4 salinity [g kg-1] " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "header = pd.read_csv('Japan/CTD_Japan_station_1.csv', nrows=5, header=None)\n", + "header" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Read in data\n", + "P, T, S = np.loadtxt('Japan/CTD_Japan_station_1.csv', delimiter=\",\", skiprows=5, unpack=True)\n", + "# for simplicity we assume a depth-pressure conversion factor of 1 (dbar = 1 m)\n", + "z = P*10 # convert pressure from hPa to m\n", + "\n", + "# Plot the data\n", + "fig, ax1 = plt.subplots(1, 1)\n", + "\n", + "# Plot temperature on the first x-axis in blue\n", + "ax1.plot(T, z, color='blue')\n", + "ax1.invert_yaxis() # depth increases downwards\n", + "ax1.set_xlabel('Temperature', color='blue')\n", + "ax1.set_ylabel('Depth')\n", + "\n", + "# Plot salinity on the second x-axis in red\n", + "ax2 = ax1.twiny() # ax1 and ax2 share y-axis\n", + "ax2.plot(S, z, color='red')\n", + "ax2.set_xlabel('Salinity', color='red')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Thermocline\n", + "The thermocline is typically characterized by a sharp decrease in temperature compared to the layers above and below it. Look at your temperature vs. depth plot and compare the temperature characteristics of the potential thermocline to the layers above and below. The thermocline should stand out as a distinct transition layer where the temperature gradient is steeper. In addition to temperature, consider other parameters such as salinity. The thermocline is often associated with changes in other properties as well. Analyzing multiple variables can help you indentify the thermocline and provide a more comprehensive understanding of the ocean's vertical structure. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Xarray\n", + "To load the data in _xarray's_ useful format, we first create a new dataset with empty data. The dimensions _trajectory_ and _obs_ represent the length of each measurement and the number of measurements, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# To load the data in _xarray's_ useful format, we first create a new dataset with empty data. The dimensions _trajectory_ and _obs_ represent the length of each measurement and the number of measurements, respectively.\n", + "ctd = xr.Dataset(\n", + " data_vars={\n", + " 'P':(('trajectory','obs'),np.zeros((399,5)),{'long_name':'pressure','units':'hPa'}),\n", + " 'T':(('trajectory','obs'),np.zeros((399,5)),{'long_name':'temperature','units':'degC'}),\n", + " 'S':(('trajectory','obs'),np.zeros((399,5)),{'long_name':'salinity','units':'g kg-1'}),\n", + " },\n", + " coords={\n", + " 'trajectory':('trajectory',range(399)),\n", + " 'obs':('obs',range(1,6)),\n", + " 'lat':('obs',np.zeros((5)),{'long_name':'latitude','units':'degrees_north'}),\n", + " 'lon':('obs',np.zeros((5)),{'long_name':'longitude','units':'degrees_east'}),\n", + " 'start_time':('obs',np.zeros((5), dtype='" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot vertical cross sections of temperature, salinity and density with longitude and depth on the x- and y-axis, respectively.\n", + "fig = plt.figure(figsize=(12,6))\n", + "ax1 = fig.add_subplot(131)\n", + "ax2 = fig.add_subplot(132, sharey=ax1)\n", + "ax2.yaxis.set_visible(False)\n", + "ax3 = fig.add_subplot(133, sharey=ax1)\n", + "ax3.yaxis.tick_right()\n", + "ax3.yaxis.set_label_position('right')\n", + "ctd_interp.T.plot(ax=ax1,x='lon',y='z',cmap=cmocean.cm.thermal,\n", + " cbar_kwargs={'orientation':'horizontal'})\n", + "ctd_interp.S.plot(ax=ax2,x='lon',y='z',cmap=cmocean.cm.haline,\n", + " cbar_kwargs={'orientation':'horizontal'})\n", + "ctd_interp.Rho.plot(ax=ax3,x='lon',y='z',cmap=cmocean.cm.dense,\n", + " cbar_kwargs={'orientation':'horizontal'})\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'vertical shear of meridional velocity')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate and plot the vertical shear of the meridional velocity, assuming the flow is in thermal wind balance.\n", + "Re = 6371000 # radius earth [m]\n", + "Om = 2*np.pi/(86400) # angular frequency earth [s-1]\n", + "\n", + "latr = np.deg2rad(ctd_interp.lat) # latitude (rad)\n", + "lonr = np.deg2rad(ctd_interp.lon) # longitude (rad)\n", + "dlonr = lonr.diff('obs') # longitude difference between casts (rad)\n", + "dx = Re*np.cos(latr)*dlonr # distance between cast locations (m)\n", + "f = 2*Om*np.sin(latr) # coriolis parameter\n", + "\n", + "rho = ctd_interp.Rho # density (kg m-3)\n", + "drho = rho.diff('obs') # density difference between casts (kg m-3)\n", + "dvdz = -g/(f*rho) * drho/dx # vertical shear of meridional flow (m s-1)\n", + "\n", + "# plot the vertical shear of meridional velocity\n", + "dvdz.plot.pcolormesh(x='lon',y='z',cbar_kwargs={'label':r'dv/dz [s$^{-1}$]'})\n", + "plt.title('vertical shear of meridional velocity')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ship", + "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": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index 2fb50d36..db7542b3 100644 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -2,4 +2,6 @@ ```{nbgallery} my_tutorial.ipynb +ADCP_data_tutorial.ipynb +CTD_data_tutorial.ipynb ``` From bd1f2a835e48f80e915bf733140a41a1d89d6ee7 Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Thu, 7 Nov 2024 11:20:03 +0100 Subject: [PATCH 02/23] formatting etc --- docs/tutorials/ADCP_data_tutorial.ipynb | 120 +++++++------ docs/tutorials/CTD_data_tutorial.ipynb | 223 +++++++++++++++--------- 2 files changed, 204 insertions(+), 139 deletions(-) diff --git a/docs/tutorials/ADCP_data_tutorial.ipynb b/docs/tutorials/ADCP_data_tutorial.ipynb index d0db60a0..a85bb986 100644 --- a/docs/tutorials/ADCP_data_tutorial.ipynb +++ b/docs/tutorials/ADCP_data_tutorial.ipynb @@ -6,7 +6,7 @@ "source": [ "## Example ADCP data handling \n", "### Upwelling near Peru\n", - "The ocean region near Peru is known for its upwelling, causing the rise of nutrient-rich bottom water to the surface and a subsequently flourishing ecosystem. Hence, it is an important region for wildlife and for the fishery industry. \n", + "The ocean region near Peru is known for its upwelling, causing the rise of nutrient-rich bottom water to the surface and a subsequently flourishing ecosystem. Hence, it is an important region for wildlife and the fishery industry. \n", "\n", "We explored this area with an ADCP device onboard the Virtualship. It measured zonal and meridional velocities up to 500 metres depth. In this tutorial you can see how we process the data in the context of wind-induced effects on ocean circulation." ] @@ -44,19 +44,20 @@ "import requests\n", "\n", "files = {\n", - " \"Peru_adcp.zip\":\"https://surfdrive.surf.nl/files/index.php/s/z1qAN6VP7zbbC3z/download\"}\n", + " \"Peru_adcp.zip\": \"https://surfdrive.surf.nl/files/index.php/s/z1qAN6VP7zbbC3z/download\"\n", + "}\n", "\n", "for filename, url in files.items():\n", - " response = requests.get(url, allow_redirects=True)\n", + " response = requests.get(url, allow_redirects=True)\n", "\n", - " if response.status_code == 200:\n", - " with open(filename, \"wb\") as f:\n", - " f.write(response.content)\n", - " else:\n", - " print(\"Failed to download\", url)\n", - "print('Download ready')\n", + " if response.status_code == 200:\n", + " with open(filename, \"wb\") as f:\n", + " f.write(response.content)\n", + " else:\n", + " print(\"Failed to download\", url)\n", + "print(\"Download ready\")\n", "\n", - "#unpack the downloaded data\n", + "# unpack the downloaded data\n", "!unzip -q Peru_adcp.zip -d . " ] }, @@ -88,19 +89,19 @@ ], "source": [ "# Make a plot of the ship's trajectory. To check that it runs perpendicular to the coast of Peru.\n", - "peru = xr.open_zarr('Peru_adcp.zarr').compute()\n", + "peru = xr.open_zarr(\"Peru_adcp.zarr\").compute()\n", "\n", "# for convenience, define z positive upward\n", - "zu = np.unique(-peru.z, axis=1).squeeze() # convert z to 1D and positive upward\n", - "peru = peru.assign_coords({'z':('trajectory',zu)}).sortby('z')\n", + "zu = np.unique(-peru.z, axis=1).squeeze() # convert z to 1D and positive upward\n", + "peru = peru.assign_coords({\"z\": (\"trajectory\", zu)}).sortby(\"z\")\n", "\n", "# plot track\n", "ax = plt.axes(projection=ccrs.PlateCarree())\n", "ax.scatter(peru.lon, peru.lat, transform=ccrs.PlateCarree())\n", "ax.coastlines()\n", "ax.gridlines(draw_labels=True)\n", - "ax.set_extent((-85,-75,-15,-5))\n", - "ax.set_title('Peru ADCP data')" + "ax.set_extent((-85, -75, -15, -5))\n", + "ax.set_title(\"Peru ADCP data\")" ] }, { @@ -120,12 +121,13 @@ "# Calculate the distance from each measurement to the coast and add it as variable to the dataset.\n", "peru_surf = peru.isel(trajectory=0).compute()\n", "d = xr.zeros_like(peru_surf.lon)\n", - "lon0,lat0 = peru_surf.lon.data[-1], peru_surf.lat.data[-1]\n", - "for ob,(lon,lat) in enumerate(zip(peru_surf.lon.data,peru_surf.lat.data)):\n", - " d[ob] = distance.distance((lon,lat),(lon0,lat0)).m\n", - "peru['s'] = -d.assign_attrs({\n", - " 'long_name':'distance to coast','units':'m','positive':'shoreward'})\n", - "peru = peru.set_coords('s').sortby('s')\n", + "lon0, lat0 = peru_surf.lon.data[-1], peru_surf.lat.data[-1]\n", + "for ob, (lon, lat) in enumerate(zip(peru_surf.lon.data, peru_surf.lat.data)):\n", + " d[ob] = distance.distance((lon, lat), (lon0, lat0)).m\n", + "peru[\"s\"] = -d.assign_attrs(\n", + " {\"long_name\": \"distance to coast\", \"units\": \"m\", \"positive\": \"shoreward\"}\n", + ")\n", + "peru = peru.set_coords(\"s\").sortby(\"s\")\n", "print(f\"max distance from coast: {abs(peru.s.min()).data/1000:.2f} km\")" ] }, @@ -159,42 +161,48 @@ "# Calculate and plot the velocity components parallel and perpendicular to the coast.\n", "\n", "# calculations\n", - "dlon = np.deg2rad(peru_surf.lon.differentiate('obs'))\n", - "dlat = np.deg2rad(peru_surf.lat.differentiate('obs'))\n", + "dlon = np.deg2rad(peru_surf.lon.differentiate(\"obs\"))\n", + "dlat = np.deg2rad(peru_surf.lat.differentiate(\"obs\"))\n", "lat = np.deg2rad(peru_surf.lat)\n", - "alpha = np.arctan(dlat/(dlon*np.cos(lat))).mean('obs') # cruise direction angle\n", - "Ucross = np.cos(alpha)*peru.U + np.sin(alpha)*peru.V # cross-shore vel\n", - "Ulong = -np.sin(alpha)*peru.U + np.cos(alpha)*peru.V # long-shore vel\n", - "peru['Ucross'] = Ucross.assign_attrs({'long_name':'cross-shore velocity','units':'m s-1'})\n", - "peru['Ulong'] = Ulong.assign_attrs({'long_name':'longshore velocity','units':'m s-1'})\n", + "alpha = np.arctan(dlat / (dlon * np.cos(lat))).mean(\"obs\") # cruise direction angle\n", + "Ucross = np.cos(alpha) * peru.U + np.sin(alpha) * peru.V # cross-shore vel\n", + "Ulong = -np.sin(alpha) * peru.U + np.cos(alpha) * peru.V # long-shore vel\n", + "peru[\"Ucross\"] = Ucross.assign_attrs(\n", + " {\"long_name\": \"cross-shore velocity\", \"units\": \"m s-1\"}\n", + ")\n", + "peru[\"Ulong\"] = Ulong.assign_attrs(\n", + " {\"long_name\": \"longshore velocity\", \"units\": \"m s-1\"}\n", + ")\n", "\n", "# figure settings\n", - "fig = plt.figure(figsize=(10,6))\n", + "fig = plt.figure(figsize=(10, 6))\n", "norm = mcolors.CenteredNorm(halfrange=0.35)\n", "cmap = cmocean.cm.balance\n", - "landmask = xr.where(((peru.U==0) & (peru.V==0)), 1, np.nan)\n", + "landmask = xr.where(((peru.U == 0) & (peru.V == 0)), 1, np.nan)\n", "S = peru.s.broadcast_like(peru.lon)\n", "\n", "# cross-shore velocity plot\n", "ax1 = fig.add_subplot(121)\n", - "p = ax1.pcolormesh(S/1000, peru.z, peru.Ucross, norm=norm, cmap=cmap)\n", - "ax1.pcolormesh(S/1000, peru.z, landmask, cmap='binary_r')\n", - "ax1.set_ylabel('depth [m]')\n", - "ax1.set_xlabel('distance from coast [km]')\n", - "ax1.set_title('cross-shore velocity (positive shoreward)')\n", + "p = ax1.pcolormesh(S / 1000, peru.z, peru.Ucross, norm=norm, cmap=cmap)\n", + "ax1.pcolormesh(S / 1000, peru.z, landmask, cmap=\"binary_r\")\n", + "ax1.set_ylabel(\"depth [m]\")\n", + "ax1.set_xlabel(\"distance from coast [km]\")\n", + "ax1.set_title(\"cross-shore velocity (positive shoreward)\")\n", "ax1.grid()\n", "\n", "# alongshore velocity plot\n", "ax2 = fig.add_subplot(122, sharey=ax1)\n", "ax2.yaxis.tick_right()\n", - "p2 = ax2.pcolormesh(S/1000, peru.z, peru.Ulong, norm=norm, cmap=cmap)\n", - "ax2.pcolormesh(S/1000, peru.z, landmask, cmap='binary_r')\n", - "ax2.set_ylabel('depth [m]', rotation=270, labelpad=15)\n", - "ax2.set_xlabel('distance from coast [km]')\n", - "ax2.set_title('longshore velocity (positive with coast on r.h.s.)')\n", - "ax2.yaxis.set_label_position('right')\n", + "p2 = ax2.pcolormesh(S / 1000, peru.z, peru.Ulong, norm=norm, cmap=cmap)\n", + "ax2.pcolormesh(S / 1000, peru.z, landmask, cmap=\"binary_r\")\n", + "ax2.set_ylabel(\"depth [m]\", rotation=270, labelpad=15)\n", + "ax2.set_xlabel(\"distance from coast [km]\")\n", + "ax2.set_title(\"longshore velocity (positive with coast on r.h.s.)\")\n", + "ax2.yaxis.set_label_position(\"right\")\n", "ax2.grid()\n", - "fig.colorbar(p, ax=[ax1,ax2], shrink=0.7, orientation='horizontal', label=r'm s$^{-1}$')" + "fig.colorbar(\n", + " p, ax=[ax1, ax2], shrink=0.7, orientation=\"horizontal\", label=r\"m s$^{-1}$\"\n", + ")" ] }, { @@ -234,32 +242,36 @@ ], "source": [ "# Integrate the continuity equation to find the vertical velocity, starting from the surface downward.\n", - "peru_d = peru.sortby('z', ascending=False) # sort downward\n", + "peru_d = peru.sortby(\"z\", ascending=False) # sort downward\n", "\n", "# # compute dwdz - easy central difference method\n", "# dwdz = -peru_d.Ucross.differentiate('s')\n", "\n", "# compute dwdz - central difference using interpolated values at cell edges\n", "# slightly more accurate\n", - "landfilter = xr.where(((peru_d.U==0) & (peru_d.V==0)), 0, 1) # 0 in land, 1 in ocean\n", - "Ucrossi = peru_d.Ucross.interp(obs=peru_d.obs-0.5) # U at cell boundaries (toward ocean)\n", + "landfilter = xr.where(\n", + " ((peru_d.U == 0) & (peru_d.V == 0)), 0, 1\n", + ") # 0 in land, 1 in ocean\n", + "Ucrossi = peru_d.Ucross.interp(\n", + " obs=peru_d.obs - 0.5\n", + ") # U at cell boundaries (toward ocean)\n", "Ucrossi = Ucrossi * landfilter.data\n", - "dUcrossds = Ucrossi.diff('obs') / Ucrossi.s.diff('obs')\n", + "dUcrossds = Ucrossi.diff(\"obs\") / Ucrossi.s.diff(\"obs\")\n", "dwdz = -dUcrossds\n", "\n", "# integrate dwdz\n", - "peru['w'] = dwdz.cumulative_integrate('z')\n", + "peru[\"w\"] = dwdz.cumulative_integrate(\"z\")\n", "\n", "# plot the vertical velocity\n", "ax = plt.axes()\n", - "S,Z = xr.broadcast(peru.s/1000, peru.z)\n", + "S, Z = xr.broadcast(peru.s / 1000, peru.z)\n", "w = ax.pcolormesh(S, Z, peru.w.T, norm=mcolors.CenteredNorm(), cmap=cmap)\n", - "ax.pcolormesh(S, Z, landmask.T, cmap='binary_r')\n", - "plt.title('vertical velocity')\n", - "plt.ylabel('depth [m]')\n", - "plt.xlabel('shoreward distance [km]')\n", - "plt.xlim([-350,0])\n", - "plt.colorbar(w, label=r'm s$^{-1}$')" + "ax.pcolormesh(S, Z, landmask.T, cmap=\"binary_r\")\n", + "plt.title(\"vertical velocity\")\n", + "plt.ylabel(\"depth [m]\")\n", + "plt.xlabel(\"shoreward distance [km]\")\n", + "plt.xlim([-350, 0])\n", + "plt.colorbar(w, label=r\"m s$^{-1}$\")" ] }, { diff --git a/docs/tutorials/CTD_data_tutorial.ipynb b/docs/tutorials/CTD_data_tutorial.ipynb index b0c88b43..f7a8f0e7 100644 --- a/docs/tutorials/CTD_data_tutorial.ipynb +++ b/docs/tutorials/CTD_data_tutorial.ipynb @@ -6,7 +6,7 @@ "source": [ "## Example CTD data handling \n", "### Eddy near Japan\n", - "We explored an area near Japan with an active Eddy with the CTD device onboard the Virtualship. It measured salinity, temperature and pressure up to 2000 metres depth. In this tutorial you can see how we process the data and interpolate to create crosssections." + "We explored an area near Japan with the CTD device onboard the Virtualship. It measured salinity, temperature and pressure up to 2000 metres depth. In this tutorial you can see how we process the data and interpolate to create crosssections." ] }, { @@ -33,19 +33,21 @@ "# Download data\n", "import requests\n", "\n", - "files = {\"Japan.zip\":\"https://surfdrive.surf.nl/files/index.php/s/1EOYBwfh1y68h79/download\"}\n", + "files = {\n", + " \"Japan.zip\": \"https://surfdrive.surf.nl/files/index.php/s/1EOYBwfh1y68h79/download\"\n", + "}\n", "\n", "for filename, url in files.items():\n", - " response = requests.get(url, allow_redirects=True)\n", + " response = requests.get(url, allow_redirects=True)\n", "\n", - " if response.status_code == 200:\n", - " with open(filename, \"wb\") as f:\n", - " f.write(response.content)\n", - " else:\n", - " print(\"Failed to download\", url)\n", - "print('Download ready')\n", + " if response.status_code == 200:\n", + " with open(filename, \"wb\") as f:\n", + " f.write(response.content)\n", + " else:\n", + " print(\"Failed to download\", url)\n", + "print(\"Download ready\")\n", "\n", - "#unpack the downloaded data\n", + "# unpack the downloaded data\n", "!unzip -q Japan.zip -d . " ] }, @@ -144,7 +146,7 @@ } ], "source": [ - "header = pd.read_csv('Japan/CTD_Japan_station_1.csv', nrows=5, header=None)\n", + "header = pd.read_csv(\"Japan/CTD_Japan_station_1.csv\", nrows=5, header=None)\n", "header" ] }, @@ -166,23 +168,25 @@ ], "source": [ "# Read in data\n", - "P, T, S = np.loadtxt('Japan/CTD_Japan_station_1.csv', delimiter=\",\", skiprows=5, unpack=True)\n", + "P, T, S = np.loadtxt(\n", + " \"Japan/CTD_Japan_station_1.csv\", delimiter=\",\", skiprows=5, unpack=True\n", + ")\n", "# for simplicity we assume a depth-pressure conversion factor of 1 (dbar = 1 m)\n", - "z = P*10 # convert pressure from hPa to m\n", + "z = P * 10 # convert pressure from hPa to m\n", "\n", "# Plot the data\n", "fig, ax1 = plt.subplots(1, 1)\n", "\n", "# Plot temperature on the first x-axis in blue\n", - "ax1.plot(T, z, color='blue')\n", + "ax1.plot(T, z, color=\"blue\")\n", "ax1.invert_yaxis() # depth increases downwards\n", - "ax1.set_xlabel('Temperature', color='blue')\n", - "ax1.set_ylabel('Depth')\n", + "ax1.set_xlabel(\"Temperature\", color=\"blue\")\n", + "ax1.set_ylabel(\"Depth\")\n", "\n", "# Plot salinity on the second x-axis in red\n", "ax2 = ax1.twiny() # ax1 and ax2 share y-axis\n", - "ax2.plot(S, z, color='red')\n", - "ax2.set_xlabel('Salinity', color='red')\n", + "ax2.plot(S, z, color=\"red\")\n", + "ax2.set_xlabel(\"Salinity\", color=\"red\")\n", "\n", "plt.show()" ] @@ -212,18 +216,42 @@ "# To load the data in _xarray's_ useful format, we first create a new dataset with empty data. The dimensions _trajectory_ and _obs_ represent the length of each measurement and the number of measurements, respectively.\n", "ctd = xr.Dataset(\n", " data_vars={\n", - " 'P':(('trajectory','obs'),np.zeros((399,5)),{'long_name':'pressure','units':'hPa'}),\n", - " 'T':(('trajectory','obs'),np.zeros((399,5)),{'long_name':'temperature','units':'degC'}),\n", - " 'S':(('trajectory','obs'),np.zeros((399,5)),{'long_name':'salinity','units':'g kg-1'}),\n", + " \"P\": (\n", + " (\"trajectory\", \"obs\"),\n", + " np.zeros((399, 5)),\n", + " {\"long_name\": \"pressure\", \"units\": \"hPa\"},\n", + " ),\n", + " \"T\": (\n", + " (\"trajectory\", \"obs\"),\n", + " np.zeros((399, 5)),\n", + " {\"long_name\": \"temperature\", \"units\": \"degC\"},\n", + " ),\n", + " \"S\": (\n", + " (\"trajectory\", \"obs\"),\n", + " np.zeros((399, 5)),\n", + " {\"long_name\": \"salinity\", \"units\": \"g kg-1\"},\n", + " ),\n", " },\n", " coords={\n", - " 'trajectory':('trajectory',range(399)),\n", - " 'obs':('obs',range(1,6)),\n", - " 'lat':('obs',np.zeros((5)),{'long_name':'latitude','units':'degrees_north'}),\n", - " 'lon':('obs',np.zeros((5)),{'long_name':'longitude','units':'degrees_east'}),\n", - " 'start_time':('obs',np.zeros((5), dtype=' Date: Wed, 13 Nov 2024 02:05:57 +0100 Subject: [PATCH 03/23] Updated my_tutorial.ipynb with research proposal assignment --- docs/tutorials/my_tutorial.ipynb | 267 ++++++++++++++++++++++++++++++- 1 file changed, 263 insertions(+), 4 deletions(-) diff --git a/docs/tutorials/my_tutorial.ipynb b/docs/tutorials/my_tutorial.ipynb index 10167fd3..bf94b661 100644 --- a/docs/tutorials/my_tutorial.ipynb +++ b/docs/tutorials/my_tutorial.ipynb @@ -4,30 +4,289 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Awesome Example Tutorial" + "# Ocean and Sea Level (Research Proposal)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dear Student, \n", + "\n", + "Congratulations on receiving an early career research grant for ship time on a scientific research vessel! This achievement is a testament to your hard work and commitment to advancing marine science. The opportunity will undoubtedly enhance your research and contribute significantly to our understanding of marine science. \n", + "\n", + "You will have access to the ship for a three-week period—a duration identified as a favorable trade-off between research time and ensuring an enjoyable vessel experience. Your vessel will be equipped with two vessel-mounted ADCPs, a CTD, and can collect underway data of temperature and salinity from 2 meters below the hull of the ship. In addition, you can deploy up to 30 Argo floats and/or 30 surface drifters. A detailed plan of your route, timing and required instruments needs to be sent in for approval/grading. \n", + "\n", + "To get a taste of what it’s like to board a real vessel, you'll find some general information that is shared with all cruise participants **/LINK/Here**. You might also like to watch the video clip about life onboard the Research Vessel Southern Surveyor: [Life Onboard RV Southern Surveyor](https://www.youtube.com/watch?v=hUI0TA-gCK0)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In your grant application you’ve shown great potential, so what and where you want to research is completely up to you! But **/LINK/** here’s a list of interesting regions or phenomena to spark your imagination and you’ll get an opportunity to be in contact with experienced oceanographers. \n", + "\n", + "We’ve prepared some exercises to help you get familiar with the type of data that will be collected by the research vessel. You will be graded on your answers below. But first let’s give you some more information on the different scientific instruments. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Copy from cruise_plan_info**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Derive assignments from Preparatory_exercises**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that you are familiar with the types of instruments and what type of research you could do with them, it’s time to formulate your own scientific question and methodology. \n", + "\n", + "When preparing your cruise proposal, please bear in mind the different lead times between the submission of the proposal and the expedition being carried out. A lead time of roughly one year should be considered for regular proposals. The lead time for larger expeditions is about one and a half to two years minimum. \n", + "\n", + "With respect to time management, please note that diplomatic proposals for research permits in Exclusive Economic Zones (EEZs) of other states must generally be submitted eight months prior to the start of the expedition. Work in difficult areas (for example where there is piracy and the risk of war) must be refrained from. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Outline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Project Data**\n", + "1. **Title of the proposal**\n", + "\n", + "Please choose an appropriate and concise title for the expedition that refers to the work area. \n", + "\n", + "2. **Cruise participants**\n", + "\n", + "Please provide a list of anticipated cruise participants, including student numbers. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Methodology**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. **Research question**\n", + "\n", + "Please provide a clear and measurable research question. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. **Hypothesis**\n", + "\n", + "Please explain the anticipated outcomes of your research. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. **Choice of vessel**\n", + "\n", + "Please state the preferred research vessel for this cruise. \n", + "Choose from https://www.nioz.nl/national-marine-facilities/research-vessels \n", + "\n", + "If the NIOZ vessels don’t suffice, pick any of the ships available through MFP and explain why you need this ship. https://nioz.marinefacilitiesplanning.com/programme \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "6. **Scientific equipment required**\n", + " \n", + "Please tick both on-board and external equipment needed during the cruise \n", + "- [ ] ADCP (OceanObserver max depth 1000m with bins every 24 meters) \n", + "\n", + "- [ ] ADCP (SeaSeven max depth 150m with bins every 4 meters) \n", + "\n", + "- [ ] CTD \n", + "\n", + "- [ ] Argo floats \n", + "\n", + "- [ ] Drifters \n", + "\n", + "- [ ] XBTs\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "7. **Cruise Data**\n", + "\n", + "A map and cruise plan can be created through https://nioz.marinefacilitiesplanning.com/cruiselocationplanning# " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "8. **Cruise period**\n", + "\n", + "Please state the preferred year, season, and/or month(s) for the cruise, and provide reasons for restrictions to limited periods. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "9. **Map of working area**\n", + "\n", + "Please upload a high-resolution map of the working area and stations. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "10. **Cruise departure and arrival port; and transit days**\n", + "\n", + "Please name your preferred port of departure and preferred port of arrival. \n", + "\n", + "Please enter the number of days required for transit from the preferred port of departure to the working area and the number of days required for transit from the working area to the preferred port of arrival, each rounded to the nearest whole number. Please note that transit times from the port of departure to the first station and from the last station in the working area to the port of arrival are regarded as work days at sea, so they are deducted from your three-week availability. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "11. **Working areas / EEZs**\n", + "\n", + "Please indicate all nations from which research permits would need to be obtained on the basis of planned work in the respective Exclusive Economic Zones (EEZs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12. **Scientific work program**\n", + "\n", + "Please provide a scheme with number of necessary work and in-transit days within the working areas and station times. This can be downloaded from https://nioz.marinefacilitiesplanning.com/cruiselocationplanning# using the Export button on the right. Please export coordinates in DD=Decimal Degrees. \n", + "\n", + "Please indicate at each station what instruments you want to deploy (CTD, Argo float, drifter, XBT) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "13. **Argo settings (optional)**\n", + "\n", + "If you plan to use Argo floats, please give the required depth and cycle duration. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "14. **Feasibility**\n", + "\n", + "Please explain how the methodology detailed in this proposal aims to answer your research question. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Still a work in progress\n" + ] + } + ], "source": [ "print(\"Still a work in progress\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "ship", + "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", - "version": "3.12.5" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From fdb82bf44df568656c2f8c0b9f13d5142cf9dbe0 Mon Sep 17 00:00:00 2001 From: Shaivan Bhagat Date: Wed, 13 Nov 2024 17:32:25 +0100 Subject: [PATCH 04/23] Update my_tutorial.ipynb --- docs/tutorials/my_tutorial.ipynb | 34 ++------------------------------ 1 file changed, 2 insertions(+), 32 deletions(-) diff --git a/docs/tutorials/my_tutorial.ipynb b/docs/tutorials/my_tutorial.ipynb index bf94b661..bcef07d7 100644 --- a/docs/tutorials/my_tutorial.ipynb +++ b/docs/tutorials/my_tutorial.ipynb @@ -31,7 +31,7 @@ "\n", "You will have access to the ship for a three-week period—a duration identified as a favorable trade-off between research time and ensuring an enjoyable vessel experience. Your vessel will be equipped with two vessel-mounted ADCPs, a CTD, and can collect underway data of temperature and salinity from 2 meters below the hull of the ship. In addition, you can deploy up to 30 Argo floats and/or 30 surface drifters. A detailed plan of your route, timing and required instruments needs to be sent in for approval/grading. \n", "\n", - "To get a taste of what it’s like to board a real vessel, you'll find some general information that is shared with all cruise participants **/LINK/Here**. You might also like to watch the video clip about life onboard the Research Vessel Southern Surveyor: [Life Onboard RV Southern Surveyor](https://www.youtube.com/watch?v=hUI0TA-gCK0)." + "To get a taste of what it’s like to board a real vessel, you'll find some general information that is shared with all cruise participants **/LINK/Here**. You might also like to watch the video clip about life onboard the Research Vessel Southern Surveyor: [Life Onboard RV Southern Surveyor](https://www.youtube.com/watch?v=hUl0TA-gCK0)" ] }, { @@ -140,6 +140,7 @@ "6. **Scientific equipment required**\n", " \n", "Please tick both on-board and external equipment needed during the cruise \n", + "To check a box, replace [ ] with [x].\n", "- [ ] ADCP (OceanObserver max depth 1000m with bins every 24 meters) \n", "\n", "- [ ] ADCP (SeaSeven max depth 150m with bins every 4 meters) \n", @@ -229,37 +230,6 @@ "Please explain how the methodology detailed in this proposal aims to answer your research question. " ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Still a work in progress\n" - ] - } - ], - "source": [ - "print(\"Still a work in progress\")" - ] - }, { "cell_type": "code", "execution_count": null, From 5456b2b6156d131aeb311911c708a0384f8b1d96 Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Thu, 14 Nov 2024 16:20:15 +0100 Subject: [PATCH 05/23] rename tutorial --- .../{my_tutorial.ipynb => Research_Proposal_only.ipynb} | 2 +- docs/tutorials/index.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) rename docs/tutorials/{my_tutorial.ipynb => Research_Proposal_only.ipynb} (99%) diff --git a/docs/tutorials/my_tutorial.ipynb b/docs/tutorials/Research_Proposal_only.ipynb similarity index 99% rename from docs/tutorials/my_tutorial.ipynb rename to docs/tutorials/Research_Proposal_only.ipynb index bcef07d7..47034924 100644 --- a/docs/tutorials/my_tutorial.ipynb +++ b/docs/tutorials/Research_Proposal_only.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Ocean and Sea Level (Research Proposal)" + "# Research Proposal for ECR grant" ] }, { diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index db7542b3..5a868954 100644 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -1,7 +1,7 @@ # Tutorials ```{nbgallery} -my_tutorial.ipynb +Research_Proposal_only.ipynb ADCP_data_tutorial.ipynb CTD_data_tutorial.ipynb ``` From 2beecf22712cf0a8342e2f7b194ac35a41c87391 Mon Sep 17 00:00:00 2001 From: Shaivan Bhagat Date: Mon, 18 Nov 2024 22:34:10 +0100 Subject: [PATCH 06/23] Update Research_Proposal_only.ipynb --- docs/tutorials/Research_Proposal_only.ipynb | 324 +++++++++++++++++++- 1 file changed, 310 insertions(+), 14 deletions(-) diff --git a/docs/tutorials/Research_Proposal_only.ipynb b/docs/tutorials/Research_Proposal_only.ipynb index 47034924..5502526d 100644 --- a/docs/tutorials/Research_Proposal_only.ipynb +++ b/docs/tutorials/Research_Proposal_only.ipynb @@ -31,30 +31,238 @@ "\n", "You will have access to the ship for a three-week period—a duration identified as a favorable trade-off between research time and ensuring an enjoyable vessel experience. Your vessel will be equipped with two vessel-mounted ADCPs, a CTD, and can collect underway data of temperature and salinity from 2 meters below the hull of the ship. In addition, you can deploy up to 30 Argo floats and/or 30 surface drifters. A detailed plan of your route, timing and required instruments needs to be sent in for approval/grading. \n", "\n", - "To get a taste of what it’s like to board a real vessel, you'll find some general information that is shared with all cruise participants **/LINK/Here**. You might also like to watch the video clip about life onboard the Research Vessel Southern Surveyor: [Life Onboard RV Southern Surveyor](https://www.youtube.com/watch?v=hUl0TA-gCK0)" + "To get a taste of what it’s like to board a real vessel, you'll find some general information that is shared with all cruise participants here.\n", + "\n", + "You might also like to watch the video clip about life onboard the Research Vessel Southern Surveyor: [Life Onboard RV Southern Surveyor](https://www.youtube.com/watch?v=hUl0TA-gCK0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In your grant application you’ve shown great potential, so what and where you want to research is completely up to you! But But here's a list of interesting regions or phenomena to spark your imagination, and you’ll get an opportunity to be in contact with experienced oceanographers.\n", + " a list of interesting regions or phenomena to spark your imagination and you’ll get an opportunity to be in contact with experienced oceanographers. \n", + "\n", + "We’ve prepared some exercises to help you get familiar with the type of data that will be collected by the research vessel. You will be graded on your answers below. But first let’s give you some more information on the different scientific instruments. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Measurement Options" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Underway Data \n", + "\n", + "Underway data collection, also known as underway sampling, refers to the process of collecting oceanographic and environmental data while a research vessel is in motion. This method allows scientists to continuously gather data on various parameters such as sea surface temperature and salinity. The water inlet is located onthe hull of the ship at approximately 2 meters under the surface.\n", + "\n", + "You can collect underway data during your entire cruise and may assume it doesn’t take any time to start or stop taking measurements." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### CTD\n", + "\n", + "A Conductivity, Temperature, and Depth (CTD) sensor is an instrument used to measure the physical properties of seawater in oceanographic and environmental research. It typically consists of sensors for conductivity, temperature, and pressure (which is used to derive depth).\n", + "\n", + "Throughout a CTD deployment, the research vessels needs to stay in the same location. To calculate the time needed for a CTD deployment, take the following information into account:\n", + "\n", + "Add time to lower the vessels speed to zero and set the vessel to the appropriate direction against swell, current, wind, etc = 10 minutes Add time to deploy the CTD, and to recover = 10 minutes (2*5 min) Add time for the way down and up, usually at 1 m/s (winch capacity) = twice your water depth (in seconds)\n", + "\n", + "https://youtu.be/7N2UsPDczTw?feature=shared" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# CTD Rosette\n", + "from IPython.display import HTML\n", + "\n", + "HTML(\"\"\"\n", + "\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ADCP \n", + "\n", + "An Acoustic Doppler Current Profiler (ADCP) is an instrument used to measure the speed and direction of ocean currents at various depths. It operates based on the principle of Doppler shift, where sound waves emitted by the instrument are reflected off moving particles in the water, such as plankton or sediment, and the frequency shift of the returning waves is used to calculate the velocity of the water.\n", + "\n", + "You can deploy the ADCP on transects and choose between a shallow profiler capable of providing information to a depth of 150 m every 4 meters (the 300kHz seaSeven), or a long-range profiler capable of providing about 1000m of range every 24 meters (the 38kHz Ocean Observer). You may assume it doesn’t take any time to start or end ADCP measurements.\n", + "\n", + "https://youtu.be/mDyoZagght8?feature=shared&t=38\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# What is an ADCP\n", + "HTML(\"\"\"\n", + "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In your grant application you’ve shown great potential, so what and where you want to research is completely up to you! But **/LINK/** here’s a list of interesting regions or phenomena to spark your imagination and you’ll get an opportunity to be in contact with experienced oceanographers. \n", + "## Drifters\n", + "\n", + "A surface drifter is an oceanographic instrument used to study surface currents and ocean circulation patterns. These devices are designed to drift passively with the surface currents while transmitting data on their position and temperature.\n", "\n", - "We’ve prepared some exercises to help you get familiar with the type of data that will be collected by the research vessel. You will be graded on your answers below. But first let’s give you some more information on the different scientific instruments. " + "You may assume it doesn’t take any time to deploy drifters.\n", + "\n", + "https://youtu.be/3m_CxEE8Bhs?feature=shared&t=15" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Stokes Drifter by MetOcean Telematics\n", + "HTML(\"\"\"\n", + "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**Copy from cruise_plan_info**" + "## Argo floats\n", + "\n", + "Argo floats are self-contained, battery-powered instruments equipped with sensors to measure temperature and salinity profiles from the surface down to depths of up to 2000 meters or more. These sensors provide high-resolution vertical profiles of ocean properties, allowing researchers to study ocean variability and climate change.\n", + "\n", + "The floats periodically descend to a predetermined depth, typically around 1000 meters, and then ascend to the surface, measuring temperature and salinity profiles along the way. In tutorial 2, you learn how to control the dive depth and duration.\n", + "\n", + "You may assume it doesn’t take any time to deploy Argo floats.\n", + "\n", + "https://youtu.be/IgcYQML5se4?feature=shared" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Explaining Argo Floats\n", + "from IPython.display import IFrame\n", + "\n", + "IFrame(\n", + " \"https://www.youtube.com/embed/IgcYQML5se4?si=56wrR3RZAVBh5fE7\",\n", + " width=560,\n", + " height=315,\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**Derive assignments from Preparatory_exercises**" + " **Derive assignments from Preparatory_exercises** " ] }, { @@ -109,7 +317,18 @@ "source": [ "3. **Research question**\n", "\n", - "Please provide a clear and measurable research question. " + "Please provide a clear and measurable research question. \n", + "\n", + "Here are some examples of research you can use as inspiration: \n", + "\n", + "[The Irminger Gyre: Circulation, convection, and interannual variability](https://doi.org/10.1016/j.dsr.2011.03.001)\n", + "\n", + "[Argo float observations of basin-scale deep convection in the Irminger sea during winter 2011–2012](https://doi.org/10.1016/j.dsr.2015.12.012)\n", + "\n", + "[Horizontal mixing in the Southern Ocean from Argo float trajectories](https://doi.org/10.1002/2015JC011440)\n", + "\n", + "\n", + "Note that your question does not have to be novel. The most important is that it is researchable with the expedition planned here. " ] }, { @@ -141,6 +360,8 @@ " \n", "Please tick both on-board and external equipment needed during the cruise \n", "To check a box, replace [ ] with [x].\n", + "- [ ] Underway Data\n", + " \n", "- [ ] ADCP (OceanObserver max depth 1000m with bins every 24 meters) \n", "\n", "- [ ] ADCP (SeaSeven max depth 150m with bins every 4 meters) \n", @@ -209,7 +430,89 @@ "\n", "Please provide a scheme with number of necessary work and in-transit days within the working areas and station times. This can be downloaded from https://nioz.marinefacilitiesplanning.com/cruiselocationplanning# using the Export button on the right. Please export coordinates in DD=Decimal Degrees. \n", "\n", - "Please indicate at each station what instruments you want to deploy (CTD, Argo float, drifter, XBT) " + "Please indicate at each station what instruments you want to deploy (CTD, Argo float, drifter, XBT) and take the deployment time into account. In case of the CTD the deployment time depends on the depth of the ocean. \n", + "\n", + "Here is some sample code using the bathymetry data that the virtual ship will also use. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Download ready\n" + ] + } + ], + "source": [ + "# example for plotting and querying\n", + "# Download data\n", + "import requests\n", + "\n", + "files = {\n", + " \"GLO-MFC_001_024_mask_bathy.nc\": \"https://surfdrive.surf.nl/files/index.php/s/AdbtlgP3LJv6tOn/download\",\n", + "}\n", + "\n", + "for filename, url in files.items():\n", + " response = requests.get(url, allow_redirects=True)\n", + "\n", + "if response.status_code == 200:\n", + " with open(filename, \"wb\") as f:\n", + " f.write(response.content)\n", + "\n", + "else:\n", + " print(\"Failed to download\", url)\n", + "print(\"Download ready\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3992.48388671875\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "import xarray as xr\n", + "\n", + "# Load the bathymetry data using xarray\n", + "data = xr.open_dataset(\"GLO-MFC_001_024_mask_bathy.nc\")\n", + "\n", + "# Create a bathymetry figure and axis with Cartopy projection\n", + "fig, ax = plt.subplots(figsize=(15, 5), subplot_kw={\"projection\": ccrs.PlateCarree()})\n", + "data.deptho.plot(ax=ax, cmap=\"viridis\")\n", + "\n", + "# Specify extent, add gridlines and coastlines, show plot\n", + "ax.set_extent((130, 160, -70, -40), crs=ccrs.PlateCarree())\n", + "ax.gridlines(draw_labels=True)\n", + "ax.coastlines()\n", + "plt.show()\n", + "\n", + "# Query and print the bathymetry data at the specified location\n", + "station_depth = data.deptho.sel(latitude=-50, longitude=150, method=\"nearest\")\n", + "print(station_depth.values)" ] }, { @@ -229,13 +532,6 @@ "\n", "Please explain how the methodology detailed in this proposal aims to answer your research question. " ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 79fb1efac2caa9fe6d1425fb78844f7a679a3620 Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Tue, 19 Nov 2024 11:11:26 +0100 Subject: [PATCH 07/23] add xbt + formatting --- docs/tutorials/Research_Proposal_only.ipynb | 155 +++++++++++--------- 1 file changed, 85 insertions(+), 70 deletions(-) diff --git a/docs/tutorials/Research_Proposal_only.ipynb b/docs/tutorials/Research_Proposal_only.ipynb index 5502526d..0f569509 100644 --- a/docs/tutorials/Research_Proposal_only.ipynb +++ b/docs/tutorials/Research_Proposal_only.ipynb @@ -7,6 +7,21 @@ "# Research Proposal for ECR grant" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run this script to install the required packages for the tutorial in e.g. google colab\n", + "!pip install Ipython\n", + "!pip install requests\n", + "!pip install matplotlib\n", + "!pip install cartopy\n", + "!pip install xarray\n", + "from IPython.display import HTML" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -41,7 +56,6 @@ "metadata": {}, "source": [ "In your grant application you’ve shown great potential, so what and where you want to research is completely up to you! But But here's a list of interesting regions or phenomena to spark your imagination, and you’ll get an opportunity to be in contact with experienced oceanographers.\n", - " a list of interesting regions or phenomena to spark your imagination and you’ll get an opportunity to be in contact with experienced oceanographers. \n", "\n", "We’ve prepared some exercises to help you get familiar with the type of data that will be collected by the research vessel. You will be graded on your answers below. But first let’s give you some more information on the different scientific instruments. \n" ] @@ -60,7 +74,7 @@ "source": [ "### Underway Data \n", "\n", - "Underway data collection, also known as underway sampling, refers to the process of collecting oceanographic and environmental data while a research vessel is in motion. This method allows scientists to continuously gather data on various parameters such as sea surface temperature and salinity. The water inlet is located onthe hull of the ship at approximately 2 meters under the surface.\n", + "Underway data collection, also known as underway sampling, refers to the process of collecting oceanographic and environmental data while a research vessel is in motion. This method allows scientists to continuously gather data on various parameters such as sea surface temperature and salinity. The water inlet is located on the hull of the ship at approximately 2 meters under the surface.\n", "\n", "You can collect underway data during your entire cruise and may assume it doesn’t take any time to start or stop taking measurements." ] @@ -69,21 +83,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### ADCP \n", "\n", - "### CTD\n", - "\n", - "A Conductivity, Temperature, and Depth (CTD) sensor is an instrument used to measure the physical properties of seawater in oceanographic and environmental research. It typically consists of sensors for conductivity, temperature, and pressure (which is used to derive depth).\n", - "\n", - "Throughout a CTD deployment, the research vessels needs to stay in the same location. To calculate the time needed for a CTD deployment, take the following information into account:\n", + "An Acoustic Doppler Current Profiler (ADCP) is an instrument used to measure the speed and direction of ocean currents at various depths. It operates based on the principle of Doppler shift, where sound waves emitted by the instrument are reflected off moving particles in the water, such as plankton or sediment, and the frequency shift of the returning waves is used to calculate the velocity of the water.\n", "\n", - "Add time to lower the vessels speed to zero and set the vessel to the appropriate direction against swell, current, wind, etc = 10 minutes Add time to deploy the CTD, and to recover = 10 minutes (2*5 min) Add time for the way down and up, usually at 1 m/s (winch capacity) = twice your water depth (in seconds)\n", + "You can deploy the ADCP on transects and choose between a shallow profiler capable of providing information to a depth of 150 m every 4 meters (the 300kHz seaSeven), or a long-range profiler capable of providing about 1000m of range every 24 meters (the 38kHz Ocean Observer). You may assume it doesn’t take any time to start or end ADCP measurements.\n", "\n", - "https://youtu.be/7N2UsPDczTw?feature=shared" + "https://youtu.be/mDyoZagght8?feature=shared&t=38\n" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "jupyter": { "source_hidden": true @@ -94,23 +105,45 @@ "data": { "text/html": [ "\n", - "\n" + "" ], "text/plain": [ "" ] }, - "execution_count": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# CTD Rosette\n", - "from IPython.display import HTML\n", + "# What is an ADCP\n", + "HTML(\"\"\"\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### XBT\n", + "\n", + "An eXpendable BathyThermograph (XBT) is a probe that is dropped from a ship and measures the temperature as it falls through the water. The probe is designed to fall at a known rate, so that the depth of the temperature profile can be inferred from the time since it enters the water.\n", "\n", + "You may assume it doesn’t take any time to deploy XBTs.\n", + "\n", + "https://youtu.be/gAvxRYCnSXY?feature=shared" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Launching an XBT\n", "HTML(\"\"\"\n", - "\n", + "\n", "\"\"\")" ] }, @@ -118,18 +151,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## ADCP \n", "\n", - "An Acoustic Doppler Current Profiler (ADCP) is an instrument used to measure the speed and direction of ocean currents at various depths. It operates based on the principle of Doppler shift, where sound waves emitted by the instrument are reflected off moving particles in the water, such as plankton or sediment, and the frequency shift of the returning waves is used to calculate the velocity of the water.\n", + "### CTD\n", "\n", - "You can deploy the ADCP on transects and choose between a shallow profiler capable of providing information to a depth of 150 m every 4 meters (the 300kHz seaSeven), or a long-range profiler capable of providing about 1000m of range every 24 meters (the 38kHz Ocean Observer). You may assume it doesn’t take any time to start or end ADCP measurements.\n", + "A Conductivity, Temperature, and Depth (CTD) sensor is an instrument used to measure the physical properties of seawater in oceanographic and environmental research. It typically consists of sensors for conductivity (used to derive salinity), temperature, and pressure (used to derive depth).\n", "\n", - "https://youtu.be/mDyoZagght8?feature=shared&t=38\n" + "Throughout a CTD deployment, the research vessels needs to stay in the same location. To calculate the time needed for a CTD deployment, take the following information into account:\n", + "- Add time to lower the vessels speed to zero and set the vessel to the appropriate direction against swell, current, wind, etc = 10 minutes \n", + "- Add time to deploy the CTD, and to recover = 10 minutes (2*5 min) \n", + "- Add time for the way down and up, usually at 1 m/s (winch capacity) = twice your water depth (in seconds)\n", + "\n", + "https://youtu.be/7N2UsPDczTw?feature=shared" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "jupyter": { "source_hidden": true @@ -140,28 +177,29 @@ "data": { "text/html": [ "\n", - "" + "\n" ], "text/plain": [ "" ] }, - "execution_count": 2, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# What is an ADCP\n", + "# CTD Rosette\n", "HTML(\"\"\"\n", - "\"\"\")" + "\n", + "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Drifters\n", + "### Drifters\n", "\n", "A surface drifter is an oceanographic instrument used to study surface currents and ocean circulation patterns. These devices are designed to drift passively with the surface currents while transmitting data on their position and temperature.\n", "\n", @@ -204,7 +242,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Argo floats\n", + "### Argo floats\n", "\n", "Argo floats are self-contained, battery-powered instruments equipped with sensors to measure temperature and salinity profiles from the surface down to depths of up to 2000 meters or more. These sensors provide high-resolution vertical profiles of ocean properties, allowing researchers to study ocean variability and climate change.\n", "\n", @@ -262,7 +300,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " **Derive assignments from Preparatory_exercises** " + "TODO: **Derive assignments from Preparatory_exercises** " ] }, { @@ -294,7 +332,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Project Data**\n", + "### Project Data\n", "1. **Title of the proposal**\n", "\n", "Please choose an appropriate and concise title for the expedition that refers to the work area. \n", @@ -308,7 +346,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Methodology**" + "### Methodology" ] }, { @@ -362,17 +400,17 @@ "To check a box, replace [ ] with [x].\n", "- [ ] Underway Data\n", " \n", - "- [ ] ADCP (OceanObserver max depth 1000m with bins every 24 meters) \n", + "- [ ] ADCP (OceanObserver max depth 1000m with data every 24 meters) \n", + "\n", + "- [ ] ADCP (SeaSeven max depth 150m with data every 4 meters) \n", "\n", - "- [ ] ADCP (SeaSeven max depth 150m with bins every 4 meters) \n", + "- [ ] XBTs\n", "\n", "- [ ] CTD \n", "\n", "- [ ] Argo floats \n", "\n", - "- [ ] Drifters \n", - "\n", - "- [ ] XBTs\n" + "- [ ] Drifters \n" ] }, { @@ -381,7 +419,10 @@ "source": [ "7. **Cruise Data**\n", "\n", - "A map and cruise plan can be created through https://nioz.marinefacilitiesplanning.com/cruiselocationplanning# " + "A map and cruise plan can be created through https://nioz.marinefacilitiesplanning.com/cruiselocationplanning# \n", + "\n", + "Detailed information on how to use the website is comming soon\n", + "TODO" ] }, { @@ -428,28 +469,20 @@ "source": [ "12. **Scientific work program**\n", "\n", - "Please provide a scheme with number of necessary work and in-transit days within the working areas and station times. This can be downloaded from https://nioz.marinefacilitiesplanning.com/cruiselocationplanning# using the Export button on the right. Please export coordinates in DD=Decimal Degrees. \n", + "Please provide a scheme with number of necessary work and in-transit days within the working areas and station times. This can be downloaded from https://nioz.marinefacilitiesplanning.com/cruiselocationplanning# using the Export button on the right.\n", "\n", "Please indicate at each station what instruments you want to deploy (CTD, Argo float, drifter, XBT) and take the deployment time into account. In case of the CTD the deployment time depends on the depth of the ocean. \n", "\n", - "Here is some sample code using the bathymetry data that the virtual ship will also use. " + "Here is some sample code using the bathymetry data that the Virtual Ship would also use. " ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Download ready\n" - ] - } - ], + "outputs": [], "source": [ - "# example for plotting and querying\n", + "# example for plotting and querying bathymetry data\n", "# Download data\n", "import requests\n", "\n", @@ -471,27 +504,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3992.48388671875\n" - ] - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", @@ -505,7 +520,7 @@ "data.deptho.plot(ax=ax, cmap=\"viridis\")\n", "\n", "# Specify extent, add gridlines and coastlines, show plot\n", - "ax.set_extent((130, 160, -70, -40), crs=ccrs.PlateCarree())\n", + "ax.set_extent((130, 160, -70, -40), crs=ccrs.PlateCarree()) #set extent as (x0, x1, y0, y1)\n", "ax.gridlines(draw_labels=True)\n", "ax.coastlines()\n", "plt.show()\n", @@ -528,7 +543,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "14. **Feasibility**\n", + "### Feasibility\n", "\n", "Please explain how the methodology detailed in this proposal aims to answer your research question. " ] From 3e7d6aa38b400043fc485e697513d6485c979fe1 Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Tue, 19 Nov 2024 11:23:08 +0100 Subject: [PATCH 08/23] formatting --- docs/tutorials/Research_Proposal_only.ipynb | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/docs/tutorials/Research_Proposal_only.ipynb b/docs/tutorials/Research_Proposal_only.ipynb index 0f569509..1e31dcfa 100644 --- a/docs/tutorials/Research_Proposal_only.ipynb +++ b/docs/tutorials/Research_Proposal_only.ipynb @@ -373,9 +373,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "4. **Hypothesis**\n", + "4. **Study area**\n", "\n", - "Please explain the anticipated outcomes of your research. " + "Please explain why you chose this research topic and how the anticipated outcomes of your research contribute to our understanding. " ] }, { @@ -520,7 +520,9 @@ "data.deptho.plot(ax=ax, cmap=\"viridis\")\n", "\n", "# Specify extent, add gridlines and coastlines, show plot\n", - "ax.set_extent((130, 160, -70, -40), crs=ccrs.PlateCarree()) #set extent as (x0, x1, y0, y1)\n", + "ax.set_extent(\n", + " (130, 160, -70, -40), crs=ccrs.PlateCarree()\n", + ") # set extent as (x0, x1, y0, y1)\n", "ax.gridlines(draw_labels=True)\n", "ax.coastlines()\n", "plt.show()\n", From 26c4d47f00007c88d3950f1c30c652f4315aa855 Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Tue, 19 Nov 2024 12:54:12 +0100 Subject: [PATCH 09/23] fix index.md --- docs/tutorials/index.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index 5a868954..c5d71fb7 100644 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -1,7 +1,19 @@ # Tutorials ```{nbgallery} +--- +maxdepth: 2 +caption: Assignments +--- Research_Proposal_only.ipynb +``` + +```{nbgallery} +--- +maxdepth: 2 +caption: Tutorials +--- ADCP_data_tutorial.ipynb CTD_data_tutorial.ipynb ``` + From 0d4dcd46eddba6bfedfe061571ee86731a8f0272 Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Tue, 19 Nov 2024 13:50:30 +0100 Subject: [PATCH 10/23] fix broken link --- docs/tutorials/Research_Proposal_only.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/Research_Proposal_only.ipynb b/docs/tutorials/Research_Proposal_only.ipynb index 1e31dcfa..50ce39a7 100644 --- a/docs/tutorials/Research_Proposal_only.ipynb +++ b/docs/tutorials/Research_Proposal_only.ipynb @@ -363,7 +363,7 @@ "\n", "[Argo float observations of basin-scale deep convection in the Irminger sea during winter 2011–2012](https://doi.org/10.1016/j.dsr.2015.12.012)\n", "\n", - "[Horizontal mixing in the Southern Ocean from Argo float trajectories](https://doi.org/10.1002/2015JC011440)\n", + "[Horizontal mixing in the Southern Ocean from Argo float trajectories](https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2015JC011440)\n", "\n", "\n", "Note that your question does not have to be novel. The most important is that it is researchable with the expedition planned here. " From 29bfc2c779d169164b85b7b3d1d91b96fc6a7664 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 19 Nov 2024 12:51:46 +0000 Subject: [PATCH 11/23] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- docs/tutorials/index.md | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index c5d71fb7..f8575c90 100644 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -16,4 +16,3 @@ caption: Tutorials ADCP_data_tutorial.ipynb CTD_data_tutorial.ipynb ``` - From 19fc09c0e96f6ff77441e9f89d10a6a33ec610e6 Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Tue, 19 Nov 2024 14:31:58 +0100 Subject: [PATCH 12/23] fix ADCP video time and other link --- docs/tutorials/Research_Proposal_only.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/tutorials/Research_Proposal_only.ipynb b/docs/tutorials/Research_Proposal_only.ipynb index 50ce39a7..41b561a7 100644 --- a/docs/tutorials/Research_Proposal_only.ipynb +++ b/docs/tutorials/Research_Proposal_only.ipynb @@ -46,7 +46,7 @@ "\n", "You will have access to the ship for a three-week period—a duration identified as a favorable trade-off between research time and ensuring an enjoyable vessel experience. Your vessel will be equipped with two vessel-mounted ADCPs, a CTD, and can collect underway data of temperature and salinity from 2 meters below the hull of the ship. In addition, you can deploy up to 30 Argo floats and/or 30 surface drifters. A detailed plan of your route, timing and required instruments needs to be sent in for approval/grading. \n", "\n", - "To get a taste of what it’s like to board a real vessel, you'll find some general information that is shared with all cruise participants here.\n", + "To get a taste of what it’s like to board a real vessel, you'll find some general information that is shared with all cruise participants here.\n", "\n", "You might also like to watch the video clip about life onboard the Research Vessel Southern Surveyor: [Life Onboard RV Southern Surveyor](https://www.youtube.com/watch?v=hUl0TA-gCK0)" ] @@ -55,9 +55,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In your grant application you’ve shown great potential, so what and where you want to research is completely up to you! But But here's a list of interesting regions or phenomena to spark your imagination, and you’ll get an opportunity to be in contact with experienced oceanographers.\n", + "In your grant application you’ve shown great potential, so what and where you want to research is completely up to you! But here's a list of interesting regions or phenomena to spark your imagination, and you’ll get an opportunity to be in contact with experienced oceanographers.\n", "\n", - "We’ve prepared some exercises to help you get familiar with the type of data that will be collected by the research vessel. You will be graded on your answers below. But first let’s give you some more information on the different scientific instruments. \n" + "We’ve prepared some exercises to help you get familiar with the type of data that will be collected by the research vessel. But first let’s give you some more information on the different scientific instruments. \n" ] }, { @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "jupyter": { "source_hidden": true @@ -119,7 +119,7 @@ "source": [ "# What is an ADCP\n", "HTML(\"\"\"\n", - "\"\"\")" + "\"\"\")" ] }, { From ecd8c08c5863bdce17d0c1c8c09d3e742a6df013 Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Wed, 20 Nov 2024 09:23:03 +0100 Subject: [PATCH 13/23] add Iurys suggestions --- docs/tutorials/Research_Proposal_only.ipynb | 184 ++------------------ 1 file changed, 14 insertions(+), 170 deletions(-) diff --git a/docs/tutorials/Research_Proposal_only.ipynb b/docs/tutorials/Research_Proposal_only.ipynb index 41b561a7..70360b5a 100644 --- a/docs/tutorials/Research_Proposal_only.ipynb +++ b/docs/tutorials/Research_Proposal_only.ipynb @@ -14,12 +14,10 @@ "outputs": [], "source": [ "# Run this script to install the required packages for the tutorial in e.g. google colab\n", - "!pip install Ipython\n", - "!pip install requests\n", - "!pip install matplotlib\n", "!pip install cartopy\n", - "!pip install xarray\n", - "from IPython.display import HTML" + "!pip install matplotlib\n", + "!pip install requests\n", + "!pip install xarray" ] }, { @@ -44,11 +42,12 @@ "\n", "Congratulations on receiving an early career research grant for ship time on a scientific research vessel! This achievement is a testament to your hard work and commitment to advancing marine science. The opportunity will undoubtedly enhance your research and contribute significantly to our understanding of marine science. \n", "\n", - "You will have access to the ship for a three-week period—a duration identified as a favorable trade-off between research time and ensuring an enjoyable vessel experience. Your vessel will be equipped with two vessel-mounted ADCPs, a CTD, and can collect underway data of temperature and salinity from 2 meters below the hull of the ship. In addition, you can deploy up to 30 Argo floats and/or 30 surface drifters. A detailed plan of your route, timing and required instruments needs to be sent in for approval/grading. \n", + "You will have access to the ship for a three-week period—a duration identified as a favorable trade-off between research time and ensuring an enjoyable vessel experience. Your vessel will be equipped with two vessel-mounted ADCPs, a CTD, and can collect underway data of temperature and salinity from 2 meters below the hull of the ship. In addition, you can deploy XBTs and up to 30 Argo floats and/or 30 surface drifters. A detailed plan of your route, timing and required instruments needs to be sent in for approval/grading. \n", "\n", "To get a taste of what it’s like to board a real vessel, you'll find some general information that is shared with all cruise participants here.\n", "\n", - "You might also like to watch the video clip about life onboard the Research Vessel Southern Surveyor: [Life Onboard RV Southern Surveyor](https://www.youtube.com/watch?v=hUl0TA-gCK0)" + "You might also like to watch the video clip about [Life on board a research ship (RRS Discovery)](https://youtu.be/IHM7JFWg9qw?feature=shared) or the one below. \n", + "" ] }, { @@ -89,37 +88,7 @@ "\n", "You can deploy the ADCP on transects and choose between a shallow profiler capable of providing information to a depth of 150 m every 4 meters (the 300kHz seaSeven), or a long-range profiler capable of providing about 1000m of range every 24 meters (the 38kHz Ocean Observer). You may assume it doesn’t take any time to start or end ADCP measurements.\n", "\n", - "https://youtu.be/mDyoZagght8?feature=shared&t=38\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# What is an ADCP\n", - "HTML(\"\"\"\n", - "\"\"\")" + "" ] }, { @@ -132,19 +101,7 @@ "\n", "You may assume it doesn’t take any time to deploy XBTs.\n", "\n", - "https://youtu.be/gAvxRYCnSXY?feature=shared" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Launching an XBT\n", - "HTML(\"\"\"\n", - "\n", - "\"\"\")" + "" ] }, { @@ -161,38 +118,7 @@ "- Add time to deploy the CTD, and to recover = 10 minutes (2*5 min) \n", "- Add time for the way down and up, usually at 1 m/s (winch capacity) = twice your water depth (in seconds)\n", "\n", - "https://youtu.be/7N2UsPDczTw?feature=shared" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# CTD Rosette\n", - "HTML(\"\"\"\n", - "\n", - "\"\"\")" + "" ] }, { @@ -205,37 +131,7 @@ "\n", "You may assume it doesn’t take any time to deploy drifters.\n", "\n", - "https://youtu.be/3m_CxEE8Bhs?feature=shared&t=15" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Stokes Drifter by MetOcean Telematics\n", - "HTML(\"\"\"\n", - "\"\"\")" + "" ] }, { @@ -250,57 +146,7 @@ "\n", "You may assume it doesn’t take any time to deploy Argo floats.\n", "\n", - "https://youtu.be/IgcYQML5se4?feature=shared" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Explaining Argo Floats\n", - "from IPython.display import IFrame\n", - "\n", - "IFrame(\n", - " \"https://www.youtube.com/embed/IgcYQML5se4?si=56wrR3RZAVBh5fE7\",\n", - " width=560,\n", - " height=315,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "TODO: **Derive assignments from Preparatory_exercises** " + "" ] }, { @@ -363,8 +209,6 @@ "\n", "[Argo float observations of basin-scale deep convection in the Irminger sea during winter 2011–2012](https://doi.org/10.1016/j.dsr.2015.12.012)\n", "\n", - "[Horizontal mixing in the Southern Ocean from Argo float trajectories](https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2015JC011440)\n", - "\n", "\n", "Note that your question does not have to be novel. The most important is that it is researchable with the expedition planned here. " ] @@ -396,7 +240,7 @@ "source": [ "6. **Scientific equipment required**\n", " \n", - "Please tick both on-board and external equipment needed during the cruise \n", + "Please tick both on-board and external equipment needed during the cruise. Note that you can only use one of the ADCPs at any time. \n", "To check a box, replace [ ] with [x].\n", "- [ ] Underway Data\n", " \n", @@ -473,7 +317,7 @@ "\n", "Please indicate at each station what instruments you want to deploy (CTD, Argo float, drifter, XBT) and take the deployment time into account. In case of the CTD the deployment time depends on the depth of the ocean. \n", "\n", - "Here is some sample code using the bathymetry data that the Virtual Ship would also use. " + "Here is some sample code using the bathymetry data that the Virtual Ship will also use. " ] }, { @@ -545,7 +389,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Feasibility\n", + "### 14. Feasibility\n", "\n", "Please explain how the methodology detailed in this proposal aims to answer your research question. " ] From 0df2185dc184cc29f659648c063f8f9fb39e7d19 Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Wed, 20 Nov 2024 16:58:08 +0100 Subject: [PATCH 14/23] minor changes for grading --- docs/tutorials/Research_Proposal_only.ipynb | 31 +++++++++------------ 1 file changed, 13 insertions(+), 18 deletions(-) diff --git a/docs/tutorials/Research_Proposal_only.ipynb b/docs/tutorials/Research_Proposal_only.ipynb index 70360b5a..395f8f0c 100644 --- a/docs/tutorials/Research_Proposal_only.ipynb +++ b/docs/tutorials/Research_Proposal_only.ipynb @@ -254,14 +254,16 @@ "\n", "- [ ] Argo floats \n", "\n", - "- [ ] Drifters \n" + "- [ ] Drifters \n", + "\n", + "Please explain why you use the instruments checked above." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "7. **Cruise Data**\n", + "### Cruise Data\n", "\n", "A map and cruise plan can be created through https://nioz.marinefacilitiesplanning.com/cruiselocationplanning# \n", "\n", @@ -273,7 +275,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "8. **Cruise period**\n", + "7. **Cruise period**\n", "\n", "Please state the preferred year, season, and/or month(s) for the cruise, and provide reasons for restrictions to limited periods. \n" ] @@ -282,7 +284,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "9. **Map of working area**\n", + "8. **Map of working area**\n", "\n", "Please upload a high-resolution map of the working area and stations. " ] @@ -291,7 +293,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "10. **Cruise departure and arrival port; and transit days**\n", + "9. **Cruise departure and arrival port; and transit days**\n", "\n", "Please name your preferred port of departure and preferred port of arrival. \n", "\n", @@ -302,7 +304,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "11. **Working areas / EEZs**\n", + "10. **Working areas / EEZs**\n", "\n", "Please indicate all nations from which research permits would need to be obtained on the basis of planned work in the respective Exclusive Economic Zones (EEZs)" ] @@ -311,11 +313,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "12. **Scientific work program**\n", + "11. **Scientific work program**\n", "\n", "Please provide a scheme with number of necessary work and in-transit days within the working areas and station times. This can be downloaded from https://nioz.marinefacilitiesplanning.com/cruiselocationplanning# using the Export button on the right.\n", "\n", - "Please indicate at each station what instruments you want to deploy (CTD, Argo float, drifter, XBT) and take the deployment time into account. In case of the CTD the deployment time depends on the depth of the ocean. \n", + "Please indicate at each station what instruments you want to deploy (CTD, Argo float, drifter, XBT) and take the deployment time into account. In case of the CTD the deployment time depends on the depth of the ocean. \n", + "\n", + "If you plan to use Argo floats, please give the required depth and cycle duration. \n", "\n", "Here is some sample code using the bathymetry data that the Virtual Ship will also use. " ] @@ -380,16 +384,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "13. **Argo settings (optional)**\n", - "\n", - "If you plan to use Argo floats, please give the required depth and cycle duration. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 14. Feasibility\n", + "### 12. Feasibility\n", "\n", "Please explain how the methodology detailed in this proposal aims to answer your research question. " ] From 391ae359ab50a5efd53a74985882a387d6ee14ab Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Tue, 26 Nov 2024 11:44:00 +0100 Subject: [PATCH 15/23] add MFP info --- docs/tutorials/Research_Proposal_only.ipynb | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/docs/tutorials/Research_Proposal_only.ipynb b/docs/tutorials/Research_Proposal_only.ipynb index 395f8f0c..14d04152 100644 --- a/docs/tutorials/Research_Proposal_only.ipynb +++ b/docs/tutorials/Research_Proposal_only.ipynb @@ -267,8 +267,8 @@ "\n", "A map and cruise plan can be created through https://nioz.marinefacilitiesplanning.com/cruiselocationplanning# \n", "\n", - "Detailed information on how to use the website is comming soon\n", - "TODO" + "Documentation on how to use the website can be found here and you can watch the video below.\n", + "" ] }, { @@ -317,11 +317,9 @@ "\n", "Please provide a scheme with number of necessary work and in-transit days within the working areas and station times. This can be downloaded from https://nioz.marinefacilitiesplanning.com/cruiselocationplanning# using the Export button on the right.\n", "\n", - "Please indicate at each station what instruments you want to deploy (CTD, Argo float, drifter, XBT) and take the deployment time into account. In case of the CTD the deployment time depends on the depth of the ocean. \n", + "Please indicate at each station what instruments you want to deploy (CTD, Argo float, drifter, XBT) and take the deployment time into account. If you plan to use Argo floats, please give the required depth and cycle duration. In case of the CTD the deployment time depends on the depth of the ocean. \n", "\n", - "If you plan to use Argo floats, please give the required depth and cycle duration. \n", - "\n", - "Here is some sample code using the bathymetry data that the Virtual Ship will also use. " + "Here is some sample code to sample the depth using the bathymetry data that the Virtual Ship will also use. " ] }, { From 27c21c6466f04105d955caf5c06eb5353c500ed0 Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Tue, 26 Nov 2024 14:55:18 +0100 Subject: [PATCH 16/23] fix review comments --- docs/tutorials/Research_Proposal_only.ipynb | 60 ++++++++++----------- 1 file changed, 29 insertions(+), 31 deletions(-) diff --git a/docs/tutorials/Research_Proposal_only.ipynb b/docs/tutorials/Research_Proposal_only.ipynb index 14d04152..d2fed412 100644 --- a/docs/tutorials/Research_Proposal_only.ipynb +++ b/docs/tutorials/Research_Proposal_only.ipynb @@ -7,19 +7,6 @@ "# Research Proposal for ECR grant" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Run this script to install the required packages for the tutorial in e.g. google colab\n", - "!pip install cartopy\n", - "!pip install matplotlib\n", - "!pip install requests\n", - "!pip install xarray" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -44,7 +31,7 @@ "\n", "You will have access to the ship for a three-week period—a duration identified as a favorable trade-off between research time and ensuring an enjoyable vessel experience. Your vessel will be equipped with two vessel-mounted ADCPs, a CTD, and can collect underway data of temperature and salinity from 2 meters below the hull of the ship. In addition, you can deploy XBTs and up to 30 Argo floats and/or 30 surface drifters. A detailed plan of your route, timing and required instruments needs to be sent in for approval/grading. \n", "\n", - "To get a taste of what it’s like to board a real vessel, you'll find some general information that is shared with all cruise participants here.\n", + "To get a taste of what it’s like to board a real vessel, you'll find some general information that is shared with all cruise participants here.\n", "\n", "You might also like to watch the video clip about [Life on board a research ship (RRS Discovery)](https://youtu.be/IHM7JFWg9qw?feature=shared) or the one below. \n", "" @@ -54,9 +41,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In your grant application you’ve shown great potential, so what and where you want to research is completely up to you! But here's a list of interesting regions or phenomena to spark your imagination, and you’ll get an opportunity to be in contact with experienced oceanographers.\n", + "In your grant application you’ve shown great potential, so what and where you want to research is completely up to you! But here's a list of interesting regions or phenomena to spark your imagination, and you’ll get an opportunity to be in contact with experienced oceanographers.\n", "\n", - "We’ve prepared some exercises to help you get familiar with the type of data that will be collected by the research vessel. But first let’s give you some more information on the different scientific instruments. \n" + "We’ve prepared some exercises to help you get familiar with the type of data that will be collected by the research vessel. Research is typically done on **station** while staying stationary for a period of time, or while moving over a **transect**. So let’s give you some more information on the different scientific instruments first. \n" ] }, { @@ -75,7 +62,7 @@ "\n", "Underway data collection, also known as underway sampling, refers to the process of collecting oceanographic and environmental data while a research vessel is in motion. This method allows scientists to continuously gather data on various parameters such as sea surface temperature and salinity. The water inlet is located on the hull of the ship at approximately 2 meters under the surface.\n", "\n", - "You can collect underway data during your entire cruise and may assume it doesn’t take any time to start or stop taking measurements." + "You can collect underway data during your entire cruise or on specific transects and may assume it doesn’t take any time to start or stop taking measurements." ] }, { @@ -86,7 +73,7 @@ "\n", "An Acoustic Doppler Current Profiler (ADCP) is an instrument used to measure the speed and direction of ocean currents at various depths. It operates based on the principle of Doppler shift, where sound waves emitted by the instrument are reflected off moving particles in the water, such as plankton or sediment, and the frequency shift of the returning waves is used to calculate the velocity of the water.\n", "\n", - "You can deploy the ADCP on transects and choose between a shallow profiler capable of providing information to a depth of 150 m every 4 meters (the 300kHz seaSeven), or a long-range profiler capable of providing about 1000m of range every 24 meters (the 38kHz Ocean Observer). You may assume it doesn’t take any time to start or end ADCP measurements.\n", + "You can deploy the ADCP on transects and choose between a shallow profiler capable of providing information to a depth of 150 m every 4 meters (the 300kHz seaSeven), or a long-range profiler capable of providing about 1000m of range every 24 meters (the 38kHz Ocean Observer). You can collect ADCP data during your entire cruise or on specific transects and may assume it doesn’t take any time to start or end ADCP measurements.\n", "\n", "" ] @@ -114,9 +101,9 @@ "A Conductivity, Temperature, and Depth (CTD) sensor is an instrument used to measure the physical properties of seawater in oceanographic and environmental research. It typically consists of sensors for conductivity (used to derive salinity), temperature, and pressure (used to derive depth).\n", "\n", "Throughout a CTD deployment, the research vessels needs to stay in the same location. To calculate the time needed for a CTD deployment, take the following information into account:\n", - "- Add time to lower the vessels speed to zero and set the vessel to the appropriate direction against swell, current, wind, etc = 10 minutes \n", - "- Add time to deploy the CTD, and to recover = 10 minutes (2*5 min) \n", - "- Add time for the way down and up, usually at 1 m/s (winch capacity) = twice your water depth (in seconds)\n", + "* Add time to lower the vessels speed to zero and set the vessel to the appropriate direction against swell, current, wind, etc = 10 minutes \n", + "* Add time to deploy the CTD, and to recover = 10 minutes (2*5 min) \n", + "* Add time for the way down and up, usually at 1 m/s (winch capacity) = twice your maximum depth (in seconds)\n", "\n", "" ] @@ -127,7 +114,7 @@ "source": [ "### Drifters\n", "\n", - "A surface drifter is an oceanographic instrument used to study surface currents and ocean circulation patterns. These devices are designed to drift passively with the surface currents while transmitting data on their position and temperature.\n", + "A surface drifter is an oceanographic instrument used to study surface currents and ocean circulation patterns. These devices are designed to drift passively with the surface currents while transmitting data on their position and sea surface temperature.\n", "\n", "You may assume it doesn’t take any time to deploy drifters.\n", "\n", @@ -142,7 +129,7 @@ "\n", "Argo floats are self-contained, battery-powered instruments equipped with sensors to measure temperature and salinity profiles from the surface down to depths of up to 2000 meters or more. These sensors provide high-resolution vertical profiles of ocean properties, allowing researchers to study ocean variability and climate change.\n", "\n", - "The floats periodically descend to a predetermined depth, typically around 1000 meters, and then ascend to the surface, measuring temperature and salinity profiles along the way. In tutorial 2, you learn how to control the dive depth and duration.\n", + "The floats periodically descend to a predetermined depth, typically around 1000 meters, and then ascend to the surface, measuring temperature and salinity profiles along the way.\n", "\n", "You may assume it doesn’t take any time to deploy Argo floats.\n", "\n", @@ -153,11 +140,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that you are familiar with the types of instruments and what type of research you could do with them, it’s time to formulate your own scientific question and methodology. \n", + "Now that you are familiar with the types of instruments and what type of research you could do with them, it’s time to formulate your own scientific question and methodology. Here are some things to keep in mind: \n", + "\n", + "When preparing your cruise proposal, please bear in mind the different lead times between the submission of the proposal and the expedition being carried out. A lead time of roughly one year should be considered for regular proposals, such as this one. The lead time for longer and/or larger expeditions is about one and a half to two years minimum. \n", + "\n", + "With respect to time management, please note that diplomatic proposals for research permits in Exclusive Economic Zones (EEZs) of other states must generally be submitted eight months prior to the start of the expedition. Work in difficult areas (for example where there is piracy and the risk of war) must be refrained from. \n", + "\n", + "Keep in mind that research vessels have different polar capabilities and it therefore varies in which areas they can operate at different times of the year, depending on ice conditions and minimum allowed air temperature. \n", + "\n", + "All equipment required at sea needs to be transferred to the port of departure before leaving. Schedule enough time to prepare and pack your equipment in the office. This includes scientific instruments, sampling bottles, tools, repair material, safety gear, and any other materials essential to your work. \n", + "\n", + "Make sure that your instruments are cleaned and calibrated before packing and ensure that sensors are functioning properly, batteries are charged, and any necessary software is updated.\n", "\n", - "When preparing your cruise proposal, please bear in mind the different lead times between the submission of the proposal and the expedition being carried out. A lead time of roughly one year should be considered for regular proposals. The lead time for larger expeditions is about one and a half to two years minimum. \n", + "Finally, fit your equipment in the 20-foot container. The container will be sent to your port of departure ahead of time with a cargo boat, so make sure you are packed in time for this transfer. Remember there are no shops at sea, so think carefully and plan ahead. \n", "\n", - "With respect to time management, please note that diplomatic proposals for research permits in Exclusive Economic Zones (EEZs) of other states must generally be submitted eight months prior to the start of the expedition. Work in difficult areas (for example where there is piracy and the risk of war) must be refrained from. " + "![Equipment preparation NIOZ](https://www.nioz.nl/application/files/9116/7500/3457/2023-01-16-packing.jpg) \n", + "![Equipment loading](https://www.nioz.nl/application/files/7416/7810/2265/2023-03-06-container-shifting.jpg) " ] }, { @@ -185,7 +183,7 @@ "\n", "2. **Cruise participants**\n", "\n", - "Please provide a list of anticipated cruise participants, including student numbers. " + "Please provide a list of anticipated cruise participants. Include at least your own names. " ] }, { @@ -265,7 +263,7 @@ "source": [ "### Cruise Data\n", "\n", - "A map and cruise plan can be created through https://nioz.marinefacilitiesplanning.com/cruiselocationplanning# \n", + "A map and cruise plan can be created through [https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#](https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#)\n", "\n", "Documentation on how to use the website can be found here and you can watch the video below.\n", "" @@ -286,7 +284,7 @@ "source": [ "8. **Map of working area**\n", "\n", - "Please upload a high-resolution map of the working area and stations. " + "Please upload a high-resolution map of the oceans areas you'll research and inlcude all stations and transects. " ] }, { @@ -315,7 +313,7 @@ "source": [ "11. **Scientific work program**\n", "\n", - "Please provide a scheme with number of necessary work and in-transit days within the working areas and station times. This can be downloaded from https://nioz.marinefacilitiesplanning.com/cruiselocationplanning# using the Export button on the right.\n", + "Please provide a scheme with number of necessary work and in-transit days within the working areas and station times. This can be downloaded from [https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#](https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#) using the Export button on the right.\n", "\n", "Please indicate at each station what instruments you want to deploy (CTD, Argo float, drifter, XBT) and take the deployment time into account. If you plan to use Argo floats, please give the required depth and cycle duration. In case of the CTD the deployment time depends on the depth of the ocean. \n", "\n", @@ -382,7 +380,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 12. Feasibility\n", + "12. **Feasibility**\n", "\n", "Please explain how the methodology detailed in this proposal aims to answer your research question. " ] From 081038f7e0b15982a92d224263d156b84d4570c2 Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Tue, 26 Nov 2024 15:22:38 +0100 Subject: [PATCH 17/23] minor fixes --- docs/tutorials/Research_Proposal_only.ipynb | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/docs/tutorials/Research_Proposal_only.ipynb b/docs/tutorials/Research_Proposal_only.ipynb index d2fed412..b18fef5a 100644 --- a/docs/tutorials/Research_Proposal_only.ipynb +++ b/docs/tutorials/Research_Proposal_only.ipynb @@ -101,6 +101,7 @@ "A Conductivity, Temperature, and Depth (CTD) sensor is an instrument used to measure the physical properties of seawater in oceanographic and environmental research. It typically consists of sensors for conductivity (used to derive salinity), temperature, and pressure (used to derive depth).\n", "\n", "Throughout a CTD deployment, the research vessels needs to stay in the same location. To calculate the time needed for a CTD deployment, take the following information into account:\n", + "\n", "* Add time to lower the vessels speed to zero and set the vessel to the appropriate direction against swell, current, wind, etc = 10 minutes \n", "* Add time to deploy the CTD, and to recover = 10 minutes (2*5 min) \n", "* Add time for the way down and up, usually at 1 m/s (winch capacity) = twice your maximum depth (in seconds)\n", @@ -240,6 +241,7 @@ " \n", "Please tick both on-board and external equipment needed during the cruise. Note that you can only use one of the ADCPs at any time. \n", "To check a box, replace [ ] with [x].\n", + "\n", "- [ ] Underway Data\n", " \n", "- [ ] ADCP (OceanObserver max depth 1000m with data every 24 meters) \n", @@ -263,7 +265,7 @@ "source": [ "### Cruise Data\n", "\n", - "A map and cruise plan can be created through [https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#](https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#)\n", + "A map and cruise plan can be created through https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#\n", "\n", "Documentation on how to use the website can be found here and you can watch the video below.\n", "" @@ -313,7 +315,7 @@ "source": [ "11. **Scientific work program**\n", "\n", - "Please provide a scheme with number of necessary work and in-transit days within the working areas and station times. This can be downloaded from [https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#](https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#) using the Export button on the right.\n", + "Please provide a scheme with number of necessary work and in-transit days within the working areas and station times. This can be downloaded from https://nioz.marinefacilitiesplanning.com/cruiselocationplanning# using the Export button on the right.\n", "\n", "Please indicate at each station what instruments you want to deploy (CTD, Argo float, drifter, XBT) and take the deployment time into account. If you plan to use Argo floats, please give the required depth and cycle duration. In case of the CTD the deployment time depends on the depth of the ocean. \n", "\n", @@ -373,7 +375,7 @@ "\n", "# Query and print the bathymetry data at the specified location\n", "station_depth = data.deptho.sel(latitude=-50, longitude=150, method=\"nearest\")\n", - "print(station_depth.values)" + "print(f\"The depth at this station is: {station_depth.values}\")" ] }, { From 0a0995d45b70d36941abbe9b5d2f373871d677e6 Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Tue, 26 Nov 2024 16:35:49 +0100 Subject: [PATCH 18/23] try to fix links --- docs/tutorials/Research_Proposal_only.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/tutorials/Research_Proposal_only.ipynb b/docs/tutorials/Research_Proposal_only.ipynb index b18fef5a..61ccebf0 100644 --- a/docs/tutorials/Research_Proposal_only.ipynb +++ b/docs/tutorials/Research_Proposal_only.ipynb @@ -265,7 +265,7 @@ "source": [ "### Cruise Data\n", "\n", - "A map and cruise plan can be created through https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#\n", + "A map and cruise plan can be created through the NIOZ MFP website \n", "\n", "Documentation on how to use the website can be found here and you can watch the video below.\n", "" @@ -315,7 +315,7 @@ "source": [ "11. **Scientific work program**\n", "\n", - "Please provide a scheme with number of necessary work and in-transit days within the working areas and station times. This can be downloaded from https://nioz.marinefacilitiesplanning.com/cruiselocationplanning# using the Export button on the right.\n", + "Please provide a scheme with number of necessary work and in-transit days within the working areas and station times. This can be downloaded from the NIOZ MFP website using the Export button on the right.\n", "\n", "Please indicate at each station what instruments you want to deploy (CTD, Argo float, drifter, XBT) and take the deployment time into account. If you plan to use Argo floats, please give the required depth and cycle duration. In case of the CTD the deployment time depends on the depth of the ocean. \n", "\n", @@ -375,7 +375,7 @@ "\n", "# Query and print the bathymetry data at the specified location\n", "station_depth = data.deptho.sel(latitude=-50, longitude=150, method=\"nearest\")\n", - "print(f\"The depth at this station is: {station_depth.values}\")" + "print(f\"The depth at this station is: {station_depth.values} meters\")" ] }, { From 3a729398a6ac5fb160d9f8510c1073734cee4eab Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Wed, 27 Nov 2024 09:40:21 +0100 Subject: [PATCH 19/23] fix links? --- docs/tutorials/Research_Proposal_only.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/tutorials/Research_Proposal_only.ipynb b/docs/tutorials/Research_Proposal_only.ipynb index 61ccebf0..2121cec4 100644 --- a/docs/tutorials/Research_Proposal_only.ipynb +++ b/docs/tutorials/Research_Proposal_only.ipynb @@ -265,7 +265,7 @@ "source": [ "### Cruise Data\n", "\n", - "A map and cruise plan can be created through the NIOZ MFP website \n", + "A map and cruise plan can be created through \n", "\n", "Documentation on how to use the website can be found here and you can watch the video below.\n", "" @@ -315,7 +315,7 @@ "source": [ "11. **Scientific work program**\n", "\n", - "Please provide a scheme with number of necessary work and in-transit days within the working areas and station times. This can be downloaded from the NIOZ MFP website using the Export button on the right.\n", + "Please provide a scheme with number of necessary work and in-transit days within the working areas and station times. This can be downloaded from [the NIOZ MFP website](https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#) using the Export button on the right.\n", "\n", "Please indicate at each station what instruments you want to deploy (CTD, Argo float, drifter, XBT) and take the deployment time into account. If you plan to use Argo floats, please give the required depth and cycle duration. In case of the CTD the deployment time depends on the depth of the ocean. \n", "\n", From 2b551ae20b2b821b1d0e362504a3ee623121e560 Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Thu, 28 Nov 2024 09:38:38 +0100 Subject: [PATCH 20/23] links and contingency --- docs/tutorials/Research_Proposal_only.ipynb | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/docs/tutorials/Research_Proposal_only.ipynb b/docs/tutorials/Research_Proposal_only.ipynb index 2121cec4..e19741b2 100644 --- a/docs/tutorials/Research_Proposal_only.ipynb +++ b/docs/tutorials/Research_Proposal_only.ipynb @@ -31,9 +31,10 @@ "\n", "You will have access to the ship for a three-week period—a duration identified as a favorable trade-off between research time and ensuring an enjoyable vessel experience. Your vessel will be equipped with two vessel-mounted ADCPs, a CTD, and can collect underway data of temperature and salinity from 2 meters below the hull of the ship. In addition, you can deploy XBTs and up to 30 Argo floats and/or 30 surface drifters. A detailed plan of your route, timing and required instruments needs to be sent in for approval/grading. \n", "\n", - "To get a taste of what it’s like to board a real vessel, you'll find some general information that is shared with all cruise participants here.\n", + "To get a taste of what it’s like to board a real vessel, you'll find some [general information that is shared with all cruise participants here](https://surfdrive.surf.nl/files/index.php/s/i2u2zqNNFECbOlJ)\n", "\n", "You might also like to watch the video clip about [Life on board a research ship (RRS Discovery)](https://youtu.be/IHM7JFWg9qw?feature=shared) or the one below. \n", + "\n", "" ] }, @@ -41,7 +42,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In your grant application you’ve shown great potential, so what and where you want to research is completely up to you! But here's a list of interesting regions or phenomena to spark your imagination, and you’ll get an opportunity to be in contact with experienced oceanographers.\n", + "In your grant application you’ve shown great potential, so what and where you want to research is completely up to you! But [here's a list of interesting regions or phenomena](https://surfdrive.surf.nl/files/index.php/s/U1dQKsjg9MkFNvm) to spark your imagination, and you’ll get an opportunity to be in contact with experienced oceanographers.\n", "\n", "We’ve prepared some exercises to help you get familiar with the type of data that will be collected by the research vessel. Research is typically done on **station** while staying stationary for a period of time, or while moving over a **transect**. So let’s give you some more information on the different scientific instruments first. \n" ] @@ -265,9 +266,9 @@ "source": [ "### Cruise Data\n", "\n", - "A map and cruise plan can be created through \n", + "A map and cruise plan can be created through [the NIOZ MFP website](https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#)\n", "\n", - "Documentation on how to use the website can be found here and you can watch the video below.\n", + "Documentation on how to use the website can be found [here](https://surfdrive.surf.nl/files/index.php/s/gSNMZqyrf0Pyfo5) and you can watch the video below.\n", "" ] }, @@ -386,6 +387,15 @@ "\n", "Please explain how the methodology detailed in this proposal aims to answer your research question. " ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "13. **Contingency**\n", + "\n", + "Please explain which stations/measurements you will skip in case of unforseen circumstances that delay your planning. " + ] } ], "metadata": { From cba8923ec4661c59c3ea2310e00aa8a337da01ad Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Thu, 28 Nov 2024 10:41:22 +0100 Subject: [PATCH 21/23] add Argo float tutorial --- docs/tutorials/Argo_float_tutorial.ipynb | 485 +++++++++++++++++++++++ docs/tutorials/index.md | 1 + 2 files changed, 486 insertions(+) create mode 100644 docs/tutorials/Argo_float_tutorial.ipynb diff --git a/docs/tutorials/Argo_float_tutorial.ipynb b/docs/tutorials/Argo_float_tutorial.ipynb new file mode 100644 index 00000000..1a40122d --- /dev/null +++ b/docs/tutorials/Argo_float_tutorial.ipynb @@ -0,0 +1,485 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example Argo float programming\n", + "### In the Tropical Atlantic\n", + "In this tutorial you will simulate the temperature and salinity profiles of an Argo float using the Parcels toolbox." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from parcels import FieldSet, ParticleSet, JITParticle, Variable, AdvectionRK4, ScipyParticle, StatusCode\n", + "import numpy as np\n", + "import math\n", + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "from datetime import timedelta\n", + "import cmocean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the simulation, you will need to download a NetCDF file with the hydrodynamic (velocities, temperature, salinity) data. We use data from the Copernicus Marine Environmental Monitoring Service (CMEMS) [Global Ocean 1/12° Physics Analysis and Forecast product](https://resources.marine.copernicus.eu/product-detail/GLOBAL_ANALYSIS_FORECAST_PHY_001_024/INFORMATION). For this assignment, we've compiled all the flow data you need into one file (~120MB), which you can find [here](https://surfdrive.surf.nl/files/index.php/s/Sn5KFlFAS10Hxbt/download)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download data\n", + "import requests\n", + "\n", + "files = {\n", + " \"global-analysis-forecast-phy-001-024_1641284158025.nc\": \"https://surfdrive.surf.nl/files/index.php/s/Sn5KFlFAS10Hxbt/download\"\n", + "}\n", + "\n", + "for filename, url in files.items():\n", + " response = requests.get(url, allow_redirects=True)\n", + "\n", + " if response.status_code == 200:\n", + " with open(filename, \"wb\") as f:\n", + " f.write(response.content)\n", + " else:\n", + " print(\"Failed to download\", url)\n", + "print(\"Download ready\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Execute the code below to create what's in Parcels called a `FieldSet` object\n", + "filename = 'global-analysis-forecast-phy-001-024_1641284158025.nc'\n", + "variables = {'U': 'uo', # dictionary with names of the variables in the netcdf file\n", + " 'V': 'vo',\n", + " 'S': 'so',\n", + " 'T': 'thetao'}\n", + "dimensions = {'lon': 'longitude', # dictionary with names of the dimensions in the netcdf file\n", + " 'lat': 'latitude',\n", + " 'depth': 'depth',\n", + " 'time': 'time'}\n", + "\n", + "fieldset = FieldSet.from_netcdf(filename, variables, dimensions)\n", + "fieldset.mindepth = fieldset.U.depth[0] # uppermost layer in the hydrodynamic data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can plot the two components of the flow field (at the surface) with the following code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dimensions of the data are:\n", + "lon: 181\n", + "lat: 61\n", + "depth: 41\n", + "time: 34 (2 loaded)\n" + ] + } + ], + "source": [ + "fieldset.computeTimeChunk() # first load the data in memory\n", + "\n", + "fig, ax = plt.subplots(1, 2, sharex=True, sharey=True)\n", + "p0 = ax[0].pcolormesh(fieldset.U.lon, fieldset.U.lat, fieldset.U.data[0,0,:,:], vmin=-1.5, vmax=1.5, cmap=cmocean.cm.balance)\n", + "p1 = ax[1].pcolormesh(fieldset.V.lon, fieldset.V.lat, fieldset.V.data[0,0,:,:], vmin=-1.5, vmax=1.5, cmap=cmocean.cm.balance)\n", + "ax[0].set_title('zonal velocity')\n", + "ax[1].set_title('meridional velocity')\n", + "ax[0].set_ylabel('latitude [°N]')\n", + "ax[0].set_xlabel('longitude [°E]')\n", + "ax[1].set_xlabel('longitude [°E]')\n", + "ax[1].yaxis.set_tick_params(left=False, right=True, labelleft=False, labelright=True)\n", + "ax[0].grid()\n", + "ax[1].grid()\n", + "cb = fig.colorbar(p1, ax=ax, orientation='horizontal')\n", + "cb.set_label('[m/s]')\n", + "gd = fieldset.U.grid\n", + "fig.suptitle(f\"velocity at depth {gd.depth[0]:.2f} m, {gd.timeslices[0][0].astype('datetime64[m]')}\")\n", + "plt.show()\n", + "\n", + "print(f\"The dimensions of the data are:\\nlon: {gd.xdim}\\nlat: {gd.ydim}\\ndepth: {gd.zdim}\\ntime: {len(gd.time_full)} ({gd.tdim} loaded)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Parcels uses the oceanic data in `fieldset` to represent the environment of a 'particle'. Particles are objects that by default hold information on there position and time. The behaviour of particles is determined by a kernel. A commonly used kernel is `AdvectionRK4`, which prescribes advection using a 4th order Runge-Kutta numerical scheme. The `fieldset` data is interpolated to the particle position and time, then the particle gets advected to determine velocities at the new position. This process is repeated three times in a clever way to minimize the numerical error." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A few years ago, [Mike Hart-Davis](https://www.dgfi.tum.de/en/staff/hart-davis-michael/) wrote a `Kernel` for Parcels to easily simulate the behaviour of an Argo float. That code, which is also available in [this notebook tutorial](https://docs.oceanparcels.org/en/latest/examples/tutorial_Argofloats.html), is copied in the cell below. Go through it and make sure you understand what it does. For practical purposes, you may assume `particle.depth` and `particle_ddepth` both represent the particle depth.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the new Kernel that mimics Argo vertical movement\n", + "def ArgoVerticalMovement(particle, fieldset, time):\n", + " driftdepth = 1000 # maximum depth in m\n", + " maxdepth = 2000 # maximum depth in m\n", + " vertical_speed = 0.10 # sink and rise speed in m/s\n", + " cycletime = 10 * 86400 # total time of cycle in seconds\n", + " drifttime = 9 * 86400 # time of deep drift in seconds\n", + "\n", + " if particle.cycle_phase == 0:\n", + " # Phase 0: Sinking with vertical_speed until depth is driftdepth\n", + " particle_ddepth += vertical_speed * particle.dt\n", + " if particle.depth >= driftdepth:\n", + " particle.cycle_phase = 1\n", + "\n", + " elif particle.cycle_phase == 1:\n", + " # Phase 1: Drifting at depth for drifttime seconds\n", + " particle.drift_age += particle.dt\n", + " if particle.drift_age >= drifttime:\n", + " particle.drift_age = 0 # reset drift_age for next cycle\n", + " particle.cycle_phase = 2\n", + "\n", + " elif particle.cycle_phase == 2:\n", + " # Phase 2: Sinking further to maxdepth\n", + " particle_ddepth += vertical_speed * particle.dt\n", + " if particle.depth >= maxdepth:\n", + " particle.cycle_phase = 3\n", + "\n", + " elif particle.cycle_phase == 3:\n", + " # Phase 3: Rising with vertical_speed until at surface\n", + " particle_ddepth -= vertical_speed * particle.dt\n", + " particle.cycle_age += particle.dt # solve issue of not updating cycle_age during ascent\n", + " if particle.depth <= fieldset.mindepth:\n", + " particle.depth = fieldset.mindepth\n", + " particle.temp = math.nan # reset temperature to NaN at end of sampling cycle\n", + " particle.salt = math.nan # idem\n", + " particle.cycle_phase = 4\n", + " else:\n", + " particle.temp = fieldset.T[time, particle.depth, particle.lat, particle.lon]\n", + " particle.salt = fieldset.S[time, particle.depth, particle.lat, particle.lon]\n", + "\n", + " elif particle.cycle_phase == 4:\n", + " # Phase 4: Transmitting at surface until cycletime is reached\n", + " if particle.cycle_age > cycletime:\n", + " particle.cycle_phase = 0\n", + " particle.cycle_age = 0\n", + "\n", + " if particle.state == StatusCode.Evaluate:\n", + " particle.cycle_age += particle.dt # update cycle_age\n", + "\n", + "\n", + "def KeepAtSurface(particle, fieldset, time):\n", + " # Prevent error when float reaches surface\n", + " if particle.state == StatusCode.ErrorThroughSurface:\n", + " particle.depth = fieldset.mindepth\n", + " particle.state = StatusCode.Success" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we can run this code, we also need to define a new Particle class, that has extra Variables like `cycle_phase`, `cycle_age` etc needed by the `ArgoVerticalMovement()` Kernel. \n", + "*Note that if you get a compiler error here, you could try to swap the `JITParticle` in the `ArgoParticle` class definition for `ScipyParticle`.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the ArgoParticle class\n", + "variables = [\n", + " Variable('cycle_phase', dtype=np.int32, initial=0.0),\n", + " Variable('cycle_age', dtype=np.float32, initial=0.0),\n", + " Variable('drift_age', dtype=np.float32, initial=0.0),\n", + " Variable('temp', dtype=np.float32, initial=np.nan),\n", + " Variable('salt', dtype=np.float32, initial=np.nan),\n", + "]\n", + "ArgoParticle = JITParticle.add_variables(variables)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can create a `ParticleSet`, which in this case consists of one `ArgoParticle`. We have to specify its initial position and time. Note that you could add any number of Argo floats here, all having different starting positions. We will simply use one float, however." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the ParticleSet \n", + "argoset = ParticleSet(\n", + " fieldset=fieldset, pclass=ArgoParticle, lon=[-37.09499], lat=[6.73472],\n", + " depth=fieldset.mindepth, time=np.datetime64('2019-07-25T11:14:00')\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we then combine the `ArgoVerticalMovement()` kernel with the built-in `AdvectionRK4()` (for the horizontal movement) and the `KeepAtSurface()` (for ending the ascent), we can compute the trajectory. This may take a while. \n", + "\n", + "*Note that `particle_ddepth` is a copy of `particle.depth` inside `ArgoVerticalMovement()`. On every time step, `particle.depth` is kept constant until all kernels have been executed. Only then are the depth changes in every kernel, `particle_ddepth - paricle.depth`, summed together to find the new depth. Therefore, the outcome does not depend on the ordering of the kernels.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "argoset.execute(\n", + " [ArgoVerticalMovement, AdvectionRK4, KeepAtSurface], # list of kernels to be executed\n", + " runtime=timedelta(days=31),\n", + " dt=timedelta(minutes=5),\n", + " output_file=argoset.ParticleFile(name=\"argo_float\", outputdt=timedelta(minutes=5), chunks=(1,500)),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can open the dataset and view the Argo trajectory with this sample code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the trajectory of the Argo float you've just simulated\n", + "ds_argo = xr.open_zarr('argo_float.zarr')\n", + "\n", + "x = ds_argo[\"lon\"][:].squeeze()\n", + "y = ds_argo[\"lat\"][:].squeeze()\n", + "z = ds_argo[\"z\"][:].squeeze()\n", + "\n", + "fig = plt.figure(figsize=(13,10))\n", + "ax = plt.axes(projection='3d')\n", + "ax.invert_zaxis()\n", + "p = ax.scatter(x,y,z, c=z, s=20, marker=\"o\", cmap='viridis_r', depthshade=False)\n", + "cb = plt.colorbar(p, shrink=0.5)\n", + "cb.ax.invert_yaxis()\n", + "cb.set_label('depth [m]')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's an example that makes the ascent twice as slow without changing the sink velocity and stores your data in a different file. You need to define the `argoset` again, because by default the simulation will otherwise continue where the previous run ended. To speed up the simulation you can set the `outputdt` to 30 minutes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Output files are stored in argo_float2.zarr.\n", + "100%|██████████████████████████████████████████████████████████████████| 2678400.0/2678400.0 [00:38<00:00, 68785.46it/s]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Answer\n", + "\n", + "def ArgoVerticalMovement2(particle, fieldset, time): # -- change kernel name\n", + " driftdepth = 1000 # maximum depth in m\n", + " maxdepth = 2000 # maximum depth in m\n", + " vertical_speed = 0.10 # sink and rise speed in m/s\n", + " cycletime = 10 * 86400 # total time of cycle in seconds\n", + " drifttime = 9 * 86400 # time of deep drift in seconds\n", + "\n", + " if particle.cycle_phase == 0:\n", + " # Phase 0: Sinking with vertical_speed until depth is driftdepth\n", + " particle_ddepth += vertical_speed * particle.dt\n", + " if particle.depth >= driftdepth:\n", + " particle.cycle_phase = 1\n", + "\n", + " elif particle.cycle_phase == 1:\n", + " # Phase 1: Drifting at depth for drifttime seconds\n", + " particle.drift_age += particle.dt\n", + " if particle.drift_age >= drifttime:\n", + " particle.drift_age = 0 # reset drift_age for next cycle\n", + " particle.cycle_phase = 2\n", + "\n", + " elif particle.cycle_phase == 2:\n", + " # Phase 2: Sinking further to maxdepth\n", + " particle_ddepth += vertical_speed * particle.dt\n", + " if particle.depth >= maxdepth:\n", + " particle.cycle_phase = 3\n", + "\n", + " elif particle.cycle_phase == 3:\n", + " # Phase 3: Rising with vertical_speed until at surface\n", + " particle_ddepth -= vertical_speed * particle.dt/ 2.0 # ------------- make twice as slow\n", + " particle.cycle_age += particle.dt \n", + " particle.temp = fieldset.T[time, particle.depth, particle.lat, particle.lon]\n", + " particle.salt = fieldset.S[time, particle.depth, particle.lat, particle.lon]\n", + " if particle.depth <= fieldset.mindepth:\n", + " particle.depth = fieldset.mindepth\n", + " particle.temp = math.nan # reset temperature to NaN at end of sampling cycle\n", + " particle.salt = math.nan # idem\n", + " particle.cycle_phase = 4\n", + " \n", + " elif particle.cycle_phase == 4:\n", + " # Phase 4: Transmitting at surface until cycletime is reached\n", + " if particle.cycle_age > cycletime:\n", + " particle.cycle_phase = 0\n", + " particle.cycle_age = 0\n", + "\n", + " if particle.state == StatusCode.Evaluate:\n", + " particle.cycle_age += particle.dt # update cycle_age\n", + "\n", + "\n", + "def KeepAtSurface(particle, fieldset, time):\n", + " if particle.state == StatusCode.ErrorThroughSurface:\n", + " particle.depth = fieldset.mindepth\n", + " particle.state = StatusCode.Success\n", + "\n", + "\n", + "argoset = ParticleSet( # ------------ redefine argoset, otherwise simulation continues from previous run\n", + " fieldset=fieldset, pclass=ArgoParticle, lon=[-37.09499], lat=[6.73472],\n", + " depth=fieldset.mindepth, time=np.datetime64('2019-07-25T11:14:00')\n", + ")\n", + "argoset.execute(\n", + " [ArgoVerticalMovement2, AdvectionRK4, KeepAtSurface], # ----- changed kernel\n", + " runtime=timedelta(days=31),\n", + " dt=timedelta(minutes=5),\n", + " output_file=argoset.ParticleFile(name=\"argo_float2\", outputdt=timedelta(minutes=30), chunks=(1,500)), # -- changed filename\n", + ")\n", + "\n", + "# Plot depth as a function of time for both the regular and slowly-ascending floats to check if your code works\n", + "ds_argo2 = xr.open_zarr('argo_float2.zarr')\n", + "fig = plt.figure()\n", + "plt.plot(ds_argo.time[0,:], -ds_argo.z[0,:], 'k', label='original')\n", + "plt.plot(ds_argo2.time[0,:], -ds_argo2.z[0,:], 'b:',label='edited')\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.ylabel('depth [m]')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plots of the vertical temperature and salinity profile, which were recorded during the ascents to the surface.\n", + "fig = plt.figure(figsize=(12,6))\n", + "fig.suptitle(f'Temperature, salinity and time during phase 3')\n", + "\n", + "days = (ds_argo.time[0,:]-ds_argo.time[0,0]).data.astype('timedelta64[m]').astype('int')/1440\n", + "tids3 = ~ds_argo.temp[0,:].isnull().compute() # time indices during cycle phase 3 (ascent)\n", + "\n", + "ax1 = fig.add_subplot(131)\n", + "ax1.plot(ds_argo.temp[0,:],-ds_argo.z[0,:], 'k')\n", + "ax1.grid()\n", + "ax1.set_xlabel('temperature [°C]')\n", + "ax1.set_ylabel('depth [m]')\n", + "\n", + "ax2 = fig.add_subplot(132, sharey=ax1)\n", + "ax2.plot(ds_argo.salt[0,:], -ds_argo.z[0,:], 'k')\n", + "ax2.grid()\n", + "ax2.yaxis.set_tick_params(left=False, labelleft=False)\n", + "ax2.set_xlabel('salinity [g/kg]')\n", + "\n", + "ax3 = fig.add_subplot(133, sharey=ax1)\n", + "ax3.plot(days[tids3], -ds_argo.z[0,tids3], 'k-o', ms=4)\n", + "ax3.yaxis.tick_right()\n", + "ax3.yaxis.label_position = 'right'\n", + "ax3.grid()\n", + "ax3.set_xlabel('days from launch')\n", + "ax3.set_ylabel('depth [m]', rotation=270)\n", + "plt.show()" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index f8575c90..234163ba 100644 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -15,4 +15,5 @@ caption: Tutorials --- ADCP_data_tutorial.ipynb CTD_data_tutorial.ipynb +Argo-float_tutorial.ipynb ``` From 31b50d99898c69dcb6fd386c04725880d18cf8fb Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 28 Nov 2024 09:53:01 +0000 Subject: [PATCH 22/23] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- docs/tutorials/Argo_float_tutorial.ipynb | 187 +++++++++++++++-------- 1 file changed, 123 insertions(+), 64 deletions(-) diff --git a/docs/tutorials/Argo_float_tutorial.ipynb b/docs/tutorials/Argo_float_tutorial.ipynb index 1a40122d..b3a8e06d 100644 --- a/docs/tutorials/Argo_float_tutorial.ipynb +++ b/docs/tutorials/Argo_float_tutorial.ipynb @@ -15,7 +15,15 @@ "metadata": {}, "outputs": [], "source": [ - "from parcels import FieldSet, ParticleSet, JITParticle, Variable, AdvectionRK4, ScipyParticle, StatusCode\n", + "from parcels import (\n", + " FieldSet,\n", + " ParticleSet,\n", + " JITParticle,\n", + " Variable,\n", + " AdvectionRK4,\n", + " ScipyParticle,\n", + " StatusCode,\n", + ")\n", "import numpy as np\n", "import math\n", "import xarray as xr\n", @@ -62,15 +70,19 @@ "outputs": [], "source": [ "# Execute the code below to create what's in Parcels called a `FieldSet` object\n", - "filename = 'global-analysis-forecast-phy-001-024_1641284158025.nc'\n", - "variables = {'U': 'uo', # dictionary with names of the variables in the netcdf file\n", - " 'V': 'vo',\n", - " 'S': 'so',\n", - " 'T': 'thetao'}\n", - "dimensions = {'lon': 'longitude', # dictionary with names of the dimensions in the netcdf file\n", - " 'lat': 'latitude',\n", - " 'depth': 'depth',\n", - " 'time': 'time'}\n", + "filename = \"global-analysis-forecast-phy-001-024_1641284158025.nc\"\n", + "variables = {\n", + " \"U\": \"uo\", # dictionary with names of the variables in the netcdf file\n", + " \"V\": \"vo\",\n", + " \"S\": \"so\",\n", + " \"T\": \"thetao\",\n", + "}\n", + "dimensions = {\n", + " \"lon\": \"longitude\", # dictionary with names of the dimensions in the netcdf file\n", + " \"lat\": \"latitude\",\n", + " \"depth\": \"depth\",\n", + " \"time\": \"time\",\n", + "}\n", "\n", "fieldset = FieldSet.from_netcdf(filename, variables, dimensions)\n", "fieldset.mindepth = fieldset.U.depth[0] # uppermost layer in the hydrodynamic data" @@ -111,26 +123,44 @@ } ], "source": [ - "fieldset.computeTimeChunk() # first load the data in memory\n", + "fieldset.computeTimeChunk() # first load the data in memory\n", "\n", "fig, ax = plt.subplots(1, 2, sharex=True, sharey=True)\n", - "p0 = ax[0].pcolormesh(fieldset.U.lon, fieldset.U.lat, fieldset.U.data[0,0,:,:], vmin=-1.5, vmax=1.5, cmap=cmocean.cm.balance)\n", - "p1 = ax[1].pcolormesh(fieldset.V.lon, fieldset.V.lat, fieldset.V.data[0,0,:,:], vmin=-1.5, vmax=1.5, cmap=cmocean.cm.balance)\n", - "ax[0].set_title('zonal velocity')\n", - "ax[1].set_title('meridional velocity')\n", - "ax[0].set_ylabel('latitude [°N]')\n", - "ax[0].set_xlabel('longitude [°E]')\n", - "ax[1].set_xlabel('longitude [°E]')\n", + "p0 = ax[0].pcolormesh(\n", + " fieldset.U.lon,\n", + " fieldset.U.lat,\n", + " fieldset.U.data[0, 0, :, :],\n", + " vmin=-1.5,\n", + " vmax=1.5,\n", + " cmap=cmocean.cm.balance,\n", + ")\n", + "p1 = ax[1].pcolormesh(\n", + " fieldset.V.lon,\n", + " fieldset.V.lat,\n", + " fieldset.V.data[0, 0, :, :],\n", + " vmin=-1.5,\n", + " vmax=1.5,\n", + " cmap=cmocean.cm.balance,\n", + ")\n", + "ax[0].set_title(\"zonal velocity\")\n", + "ax[1].set_title(\"meridional velocity\")\n", + "ax[0].set_ylabel(\"latitude [°N]\")\n", + "ax[0].set_xlabel(\"longitude [°E]\")\n", + "ax[1].set_xlabel(\"longitude [°E]\")\n", "ax[1].yaxis.set_tick_params(left=False, right=True, labelleft=False, labelright=True)\n", "ax[0].grid()\n", "ax[1].grid()\n", - "cb = fig.colorbar(p1, ax=ax, orientation='horizontal')\n", - "cb.set_label('[m/s]')\n", + "cb = fig.colorbar(p1, ax=ax, orientation=\"horizontal\")\n", + "cb.set_label(\"[m/s]\")\n", "gd = fieldset.U.grid\n", - "fig.suptitle(f\"velocity at depth {gd.depth[0]:.2f} m, {gd.timeslices[0][0].astype('datetime64[m]')}\")\n", + "fig.suptitle(\n", + " f\"velocity at depth {gd.depth[0]:.2f} m, {gd.timeslices[0][0].astype('datetime64[m]')}\"\n", + ")\n", "plt.show()\n", "\n", - "print(f\"The dimensions of the data are:\\nlon: {gd.xdim}\\nlat: {gd.ydim}\\ndepth: {gd.zdim}\\ntime: {len(gd.time_full)} ({gd.tdim} loaded)\")" + "print(\n", + " f\"The dimensions of the data are:\\nlon: {gd.xdim}\\nlat: {gd.ydim}\\ndepth: {gd.zdim}\\ntime: {len(gd.time_full)} ({gd.tdim} loaded)\"\n", + ")" ] }, { @@ -183,10 +213,14 @@ " elif particle.cycle_phase == 3:\n", " # Phase 3: Rising with vertical_speed until at surface\n", " particle_ddepth -= vertical_speed * particle.dt\n", - " particle.cycle_age += particle.dt # solve issue of not updating cycle_age during ascent\n", + " particle.cycle_age += (\n", + " particle.dt\n", + " ) # solve issue of not updating cycle_age during ascent\n", " if particle.depth <= fieldset.mindepth:\n", " particle.depth = fieldset.mindepth\n", - " particle.temp = math.nan # reset temperature to NaN at end of sampling cycle\n", + " particle.temp = (\n", + " math.nan\n", + " ) # reset temperature to NaN at end of sampling cycle\n", " particle.salt = math.nan # idem\n", " particle.cycle_phase = 4\n", " else:\n", @@ -226,11 +260,11 @@ "source": [ "# Define the ArgoParticle class\n", "variables = [\n", - " Variable('cycle_phase', dtype=np.int32, initial=0.0),\n", - " Variable('cycle_age', dtype=np.float32, initial=0.0),\n", - " Variable('drift_age', dtype=np.float32, initial=0.0),\n", - " Variable('temp', dtype=np.float32, initial=np.nan),\n", - " Variable('salt', dtype=np.float32, initial=np.nan),\n", + " Variable(\"cycle_phase\", dtype=np.int32, initial=0.0),\n", + " Variable(\"cycle_age\", dtype=np.float32, initial=0.0),\n", + " Variable(\"drift_age\", dtype=np.float32, initial=0.0),\n", + " Variable(\"temp\", dtype=np.float32, initial=np.nan),\n", + " Variable(\"salt\", dtype=np.float32, initial=np.nan),\n", "]\n", "ArgoParticle = JITParticle.add_variables(variables)" ] @@ -248,10 +282,14 @@ "metadata": {}, "outputs": [], "source": [ - "# Define the ParticleSet \n", + "# Define the ParticleSet\n", "argoset = ParticleSet(\n", - " fieldset=fieldset, pclass=ArgoParticle, lon=[-37.09499], lat=[6.73472],\n", - " depth=fieldset.mindepth, time=np.datetime64('2019-07-25T11:14:00')\n", + " fieldset=fieldset,\n", + " pclass=ArgoParticle,\n", + " lon=[-37.09499],\n", + " lat=[6.73472],\n", + " depth=fieldset.mindepth,\n", + " time=np.datetime64(\"2019-07-25T11:14:00\"),\n", ")" ] }, @@ -271,10 +309,16 @@ "outputs": [], "source": [ "argoset.execute(\n", - " [ArgoVerticalMovement, AdvectionRK4, KeepAtSurface], # list of kernels to be executed\n", + " [\n", + " ArgoVerticalMovement,\n", + " AdvectionRK4,\n", + " KeepAtSurface,\n", + " ], # list of kernels to be executed\n", " runtime=timedelta(days=31),\n", " dt=timedelta(minutes=5),\n", - " output_file=argoset.ParticleFile(name=\"argo_float\", outputdt=timedelta(minutes=5), chunks=(1,500)),\n", + " output_file=argoset.ParticleFile(\n", + " name=\"argo_float\", outputdt=timedelta(minutes=5), chunks=(1, 500)\n", + " ),\n", ")" ] }, @@ -303,19 +347,19 @@ ], "source": [ "# Plot the trajectory of the Argo float you've just simulated\n", - "ds_argo = xr.open_zarr('argo_float.zarr')\n", + "ds_argo = xr.open_zarr(\"argo_float.zarr\")\n", "\n", "x = ds_argo[\"lon\"][:].squeeze()\n", "y = ds_argo[\"lat\"][:].squeeze()\n", "z = ds_argo[\"z\"][:].squeeze()\n", "\n", - "fig = plt.figure(figsize=(13,10))\n", - "ax = plt.axes(projection='3d')\n", + "fig = plt.figure(figsize=(13, 10))\n", + "ax = plt.axes(projection=\"3d\")\n", "ax.invert_zaxis()\n", - "p = ax.scatter(x,y,z, c=z, s=20, marker=\"o\", cmap='viridis_r', depthshade=False)\n", + "p = ax.scatter(x, y, z, c=z, s=20, marker=\"o\", cmap=\"viridis_r\", depthshade=False)\n", "cb = plt.colorbar(p, shrink=0.5)\n", "cb.ax.invert_yaxis()\n", - "cb.set_label('depth [m]')" + "cb.set_label(\"depth [m]\")" ] }, { @@ -352,7 +396,8 @@ "source": [ "# Answer\n", "\n", - "def ArgoVerticalMovement2(particle, fieldset, time): # -- change kernel name\n", + "\n", + "def ArgoVerticalMovement2(particle, fieldset, time): # -- change kernel name\n", " driftdepth = 1000 # maximum depth in m\n", " maxdepth = 2000 # maximum depth in m\n", " vertical_speed = 0.10 # sink and rise speed in m/s\n", @@ -380,16 +425,20 @@ "\n", " elif particle.cycle_phase == 3:\n", " # Phase 3: Rising with vertical_speed until at surface\n", - " particle_ddepth -= vertical_speed * particle.dt/ 2.0 # ------------- make twice as slow\n", - " particle.cycle_age += particle.dt \n", + " particle_ddepth -= (\n", + " vertical_speed * particle.dt / 2.0\n", + " ) # ------------- make twice as slow\n", + " particle.cycle_age += particle.dt\n", " particle.temp = fieldset.T[time, particle.depth, particle.lat, particle.lon]\n", " particle.salt = fieldset.S[time, particle.depth, particle.lat, particle.lon]\n", " if particle.depth <= fieldset.mindepth:\n", " particle.depth = fieldset.mindepth\n", - " particle.temp = math.nan # reset temperature to NaN at end of sampling cycle\n", + " particle.temp = (\n", + " math.nan\n", + " ) # reset temperature to NaN at end of sampling cycle\n", " particle.salt = math.nan # idem\n", " particle.cycle_phase = 4\n", - " \n", + "\n", " elif particle.cycle_phase == 4:\n", " # Phase 4: Transmitting at surface until cycletime is reached\n", " if particle.cycle_age > cycletime:\n", @@ -407,24 +456,30 @@ "\n", "\n", "argoset = ParticleSet( # ------------ redefine argoset, otherwise simulation continues from previous run\n", - " fieldset=fieldset, pclass=ArgoParticle, lon=[-37.09499], lat=[6.73472],\n", - " depth=fieldset.mindepth, time=np.datetime64('2019-07-25T11:14:00')\n", + " fieldset=fieldset,\n", + " pclass=ArgoParticle,\n", + " lon=[-37.09499],\n", + " lat=[6.73472],\n", + " depth=fieldset.mindepth,\n", + " time=np.datetime64(\"2019-07-25T11:14:00\"),\n", ")\n", "argoset.execute(\n", " [ArgoVerticalMovement2, AdvectionRK4, KeepAtSurface], # ----- changed kernel\n", " runtime=timedelta(days=31),\n", " dt=timedelta(minutes=5),\n", - " output_file=argoset.ParticleFile(name=\"argo_float2\", outputdt=timedelta(minutes=30), chunks=(1,500)), # -- changed filename\n", + " output_file=argoset.ParticleFile(\n", + " name=\"argo_float2\", outputdt=timedelta(minutes=30), chunks=(1, 500)\n", + " ), # -- changed filename\n", ")\n", "\n", "# Plot depth as a function of time for both the regular and slowly-ascending floats to check if your code works\n", - "ds_argo2 = xr.open_zarr('argo_float2.zarr')\n", + "ds_argo2 = xr.open_zarr(\"argo_float2.zarr\")\n", "fig = plt.figure()\n", - "plt.plot(ds_argo.time[0,:], -ds_argo.z[0,:], 'k', label='original')\n", - "plt.plot(ds_argo2.time[0,:], -ds_argo2.z[0,:], 'b:',label='edited')\n", + "plt.plot(ds_argo.time[0, :], -ds_argo.z[0, :], \"k\", label=\"original\")\n", + "plt.plot(ds_argo2.time[0, :], -ds_argo2.z[0, :], \"b:\", label=\"edited\")\n", "plt.legend()\n", "plt.grid()\n", - "plt.ylabel('depth [m]')\n", + "plt.ylabel(\"depth [m]\")\n", "plt.show()" ] }, @@ -446,31 +501,35 @@ ], "source": [ "# Plots of the vertical temperature and salinity profile, which were recorded during the ascents to the surface.\n", - "fig = plt.figure(figsize=(12,6))\n", - "fig.suptitle(f'Temperature, salinity and time during phase 3')\n", + "fig = plt.figure(figsize=(12, 6))\n", + "fig.suptitle(f\"Temperature, salinity and time during phase 3\")\n", "\n", - "days = (ds_argo.time[0,:]-ds_argo.time[0,0]).data.astype('timedelta64[m]').astype('int')/1440\n", - "tids3 = ~ds_argo.temp[0,:].isnull().compute() # time indices during cycle phase 3 (ascent)\n", + "days = (ds_argo.time[0, :] - ds_argo.time[0, 0]).data.astype(\"timedelta64[m]\").astype(\n", + " \"int\"\n", + ") / 1440\n", + "tids3 = (\n", + " ~ds_argo.temp[0, :].isnull().compute()\n", + ") # time indices during cycle phase 3 (ascent)\n", "\n", "ax1 = fig.add_subplot(131)\n", - "ax1.plot(ds_argo.temp[0,:],-ds_argo.z[0,:], 'k')\n", + "ax1.plot(ds_argo.temp[0, :], -ds_argo.z[0, :], \"k\")\n", "ax1.grid()\n", - "ax1.set_xlabel('temperature [°C]')\n", - "ax1.set_ylabel('depth [m]')\n", + "ax1.set_xlabel(\"temperature [°C]\")\n", + "ax1.set_ylabel(\"depth [m]\")\n", "\n", "ax2 = fig.add_subplot(132, sharey=ax1)\n", - "ax2.plot(ds_argo.salt[0,:], -ds_argo.z[0,:], 'k')\n", + "ax2.plot(ds_argo.salt[0, :], -ds_argo.z[0, :], \"k\")\n", "ax2.grid()\n", "ax2.yaxis.set_tick_params(left=False, labelleft=False)\n", - "ax2.set_xlabel('salinity [g/kg]')\n", + "ax2.set_xlabel(\"salinity [g/kg]\")\n", "\n", "ax3 = fig.add_subplot(133, sharey=ax1)\n", - "ax3.plot(days[tids3], -ds_argo.z[0,tids3], 'k-o', ms=4)\n", + "ax3.plot(days[tids3], -ds_argo.z[0, tids3], \"k-o\", ms=4)\n", "ax3.yaxis.tick_right()\n", - "ax3.yaxis.label_position = 'right'\n", + "ax3.yaxis.label_position = \"right\"\n", "ax3.grid()\n", - "ax3.set_xlabel('days from launch')\n", - "ax3.set_ylabel('depth [m]', rotation=270)\n", + "ax3.set_xlabel(\"days from launch\")\n", + "ax3.set_ylabel(\"depth [m]\", rotation=270)\n", "plt.show()" ] } From b0881538f3bcf09f9318892d6d6e76f21758abf8 Mon Sep 17 00:00:00 2001 From: Emma Daniels Date: Thu, 28 Nov 2024 11:19:19 +0100 Subject: [PATCH 23/23] fix hypen --- docs/tutorials/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index 67df072f..db10cf56 100644 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -15,6 +15,6 @@ caption: Tutorials --- ADCP_data_tutorial.ipynb CTD_data_tutorial.ipynb -Argo-float_tutorial.ipynb +Argo_float_tutorial.ipynb Drifter_data_tutorial.ipynb ```