From c770d2c399435e59b47a86a65c67d610425a6a29 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Mon, 1 Sep 2025 17:19:52 +0200 Subject: [PATCH 1/9] CTD transect plot sample script --- docs/user-guide/tutorials/CTD_transects.ipynb | 494 ++++++++++++++++++ 1 file changed, 494 insertions(+) create mode 100644 docs/user-guide/tutorials/CTD_transects.ipynb diff --git a/docs/user-guide/tutorials/CTD_transects.ipynb b/docs/user-guide/tutorials/CTD_transects.ipynb new file mode 100644 index 00000000..1f168e78 --- /dev/null +++ b/docs/user-guide/tutorials/CTD_transects.ipynb @@ -0,0 +1,494 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cca80169", + "metadata": {}, + "source": [ + "# CTD Transect Plotting\n", + "\n", + "This notebook demonstrates a simple plotting exercise for CTD data across a transect, using the output of a VirtualShip expedition. There are example plots embedded at the end, but these will ultimately be replaced by your own versions as you work through the notebook.\n", + "\n", + "We can plot physical (temperature, salinity) or biogeochemical data (oxygen, chlorophyll, primary production, phyto/zoo-plankton, nutrients, pH) as measured by the VirtualShip `CTD` and `CTD_BGC` instruments, respectively.\n", + "\n", + "The plot(s) we will produce are simple plots which follow the trajectory of the expedition as a function of distance from the first waypoint, and are intended to be a starting point for your analysis. \n", + "\n", + "
\n", + "NOTE: This notebook assumes that each waypoint in the expedition is further from the start than the last waypoint. The code will still work if not, but the resultant plots might not be very intuitive.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "aad20bd7", + "metadata": {}, + "source": [ + "## Set up\n", + "\n", + "The first step is to import the Python packages required for post-processing the data and plotting. " + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "c7f9f2ee", + "metadata": {}, + "outputs": [], + "source": [ + "import cmocean.cm as cmo\n", + "import matplotlib.colors as mcolors\n", + "import matplotlib.patches as mpatches\n", + "import numpy as np\n", + "import xarray as xr\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "4f387780", + "metadata": {}, + "source": [ + "Next, you should set `data_dir` to be the path to your expedition results in the code block below. You should replace `\"/path/to/EXPEDITION/results/\"` with the path for your machine.\n", + "\n", + "
\n", + "Tip: You can get the path to your expedition results by navigating to to the folder in Terminal (using `cd`) and then using the `pwd` command. This will print your working directory which you can copy to the `data_dir` variable in this notebook. Don't forget to keep it as a string (in \"quotation\" marks)!\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf497101", + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = \"/path/to/EXPEDITION/results/\" # set this to be where your expedition output data is located on your (virtual) machine" + ] + }, + { + "cell_type": "markdown", + "id": "a499ebe2", + "metadata": {}, + "source": [ + "You should now consider which variable from your CTD casts you would like to plot. Which ones are available to you will depend on whether you have used the `CTD` (physical variables) or `CTD_BGC` (biogeochemical) instrument, or both. Below is a list of all valid variable choices for both instruments...\n", + "\n", + "`CTD` (physical):\n", + "- \"temperature\"\n", + "- \"salinity\"\n", + "\n", + "`CTD_BGC` (biogeochemical):\n", + "- \"oxygen\"\n", + "- \"nitrate\"\n", + "- \"phosphate\"\n", + "- \"ph\"\n", + "- \"zooplankton\"\n", + "- \"phytoplankton\"\n", + "- \"primary_production\"\n", + "- \"chlorophyll\"\n", + "\n", + "Copy one of the above to `plot_variable` below:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "8de8b4ae", + "metadata": {}, + "outputs": [], + "source": [ + "plot_variable = \"temperature\" # change this to your chosen variable" + ] + }, + { + "cell_type": "markdown", + "id": "a05fad14", + "metadata": {}, + "source": [ + "We also define the `VARIABLES` dictionary here, which we use to store some parameters for the plots (e.g. variable labels, what units each is in, and which colour map we should use for the plots).\n", + "\n", + "
\n", + "Tip: You don't need to change anything here, but should you wish to change the colour scheme (`cmap`) for any CTD variable you can do so. At the moment it's set to use relevant cmaps from the cmocean Python package, which has developed specialist colour schemes for oceanographic data applications.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "b32d2730", + "metadata": {}, + "outputs": [], + "source": [ + "VARIABLES = {\n", + " \"temperature\": {\n", + " \"cmap\": cmo.thermal,\n", + " \"label\": \"Temperature (°C)\",\n", + " \"ds_name\": \"temperature\",\n", + " },\n", + " \"salinity\": {\n", + " \"cmap\": cmo.haline,\n", + " \"label\": \"Salinity (psu)\",\n", + " \"ds_name\": \"salinity\",\n", + " },\n", + " \"oxygen\": {\n", + " \"cmap\": cmo.oxy,\n", + " \"label\": \"Dissolved oxygen (mmol m-3)\",\n", + " \"ds_name\": \"o2\",\n", + " },\n", + " \"nitrate\": {\n", + " \"cmap\": cmo.matter,\n", + " \"label\": \"Nitrate (mmol m-3)\",\n", + " \"ds_name\": \"no3\",\n", + " },\n", + " \"phosphate\": {\n", + " \"cmap\": cmo.matter,\n", + " \"label\": \"Phosphate (mmol m-3)\",\n", + " \"ds_name\": \"po4\",\n", + " },\n", + " \"ph\": {\n", + " \"cmap\": cmo.balance,\n", + " \"label\": \"pH\",\n", + " \"ds_name\": \"ph\",\n", + " },\n", + " \"zooplankton\": {\n", + " \"cmap\": cmo.algae,\n", + " \"label\": \"Total zooplankton (mmol m-3)\",\n", + " \"ds_name\": \"zooc\",\n", + " },\n", + " \"phytoplankton\": {\n", + " \"cmap\": cmo.algae,\n", + " \"label\": \"Total phytoplankton (mmol m-3)\",\n", + " \"ds_name\": \"phyc\",\n", + " },\n", + " \"primary_production\": {\n", + " \"cmap\": cmo.matter,\n", + " \"label\": \"Total primary production of phytoplankton (mg m-3 day-1)\",\n", + " \"ds_name\": \"nppv\",\n", + " },\n", + " \"chlorophyll\": {\n", + " \"cmap\": cmo.algae,\n", + " \"label\": \"Chlorophyll (mg m-3)\",\n", + " \"ds_name\": \"chl\",\n", + " },\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "6f9a5afb", + "metadata": {}, + "source": [ + "## Load data\n", + "\n", + "We are now ready to read in the data. You can carry on executing the next cells without making changes to the code..." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "13f4664b", + "metadata": {}, + "outputs": [], + "source": [ + "# load CTD data\n", + "filename = (\n", + " \"ctd.zarr\" if plot_variable in [\"temperature\", \"salinity\"] else \"ctd_bgc.zarr\"\n", + ")\n", + "ctd_ds = xr.open_dataset(f\"{data_dir}/{filename}\")\n", + "if ctd_ds[\"trajectory\"].size <= 1:\n", + " raise ValueError(\"Number of waypoints must be > 1\")" + ] + }, + { + "cell_type": "markdown", + "id": "a8201b14", + "metadata": {}, + "source": [ + "## Data post-processing\n", + "\n", + "Before we can continue, we need to do some post-processing to get it ready for plotting. Below are various helper functions which perform tasks such as calculating the distance of each waypoint from the start, capturing only the downcasts of the CTD casts, as well as some other utility methods. " + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "785b2b35", + "metadata": {}, + "outputs": [], + "source": [ + "# utility functions\n", + "\n", + "\n", + "def haversine(lon1, lat1, lon2, lat2):\n", + " \"\"\"Great-circle distance (meters) between two points.\"\"\"\n", + " lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n", + " dlon, dlat = lon2 - lon1, lat2 - lat1\n", + " a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2\n", + " c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))\n", + " return 6371000 * c\n", + "\n", + "\n", + "def distance_from_start(ds):\n", + " \"\"\"Add 'distance' variable: meters from first waypoint.\"\"\"\n", + " lon0, lat0 = (\n", + " ds.isel(trajectory=0)[\"lon\"].values[0],\n", + " ds.isel(trajectory=0)[\"lat\"].values[0],\n", + " )\n", + " d = np.zeros_like(ds[\"lon\"].values, dtype=float)\n", + " for ob, (lon, lat) in enumerate(zip(ds[\"lon\"], ds[\"lat\"], strict=False)):\n", + " d[ob] = haversine(lon, lat, lon0, lat0)\n", + " ds[\"distance\"] = xr.DataArray(\n", + " d,\n", + " dims=ds[\"lon\"].dims,\n", + " attrs={\"long_name\": \"distance from first waypoint\", \"units\": \"m\"},\n", + " )\n", + " return ds\n", + "\n", + "\n", + "def descent_only(ds, variable):\n", + " \"\"\"Extract descending CTD data (downcast), pad with NaNs for alignment.\"\"\"\n", + " min_z_idx = ds[\"z\"].argmin(\"obs\")\n", + " da_clean = []\n", + " for i, traj in enumerate(ds[\"trajectory\"].values):\n", + " idx = min_z_idx.sel(trajectory=traj).item()\n", + " descent_vals = ds[variable][\n", + " i, : idx + 1\n", + " ] # take values from surface to min_z_idx (inclusive)\n", + " da_clean.append(descent_vals)\n", + " max_len = max(len(arr[~np.isnan(arr)]) for arr in da_clean)\n", + " da_padded = np.full((ds[\"trajectory\"].size, max_len), np.nan)\n", + " for i, arr in enumerate(da_clean):\n", + " da_dropna = arr[~np.isnan(arr)]\n", + " da_padded[i, : len(da_dropna)] = da_dropna\n", + " return xr.DataArray(\n", + " da_padded,\n", + " dims=[\"trajectory\", \"obs\"],\n", + " coords={\"trajectory\": ds[\"trajectory\"], \"obs\": np.arange(max_len)},\n", + " )\n", + "\n", + "\n", + "def build_masked_array(data_up, profile_indices, n_profiles):\n", + " arr = np.full((n_profiles, data_up.shape[1]), np.nan)\n", + " for i, idx in enumerate(profile_indices):\n", + " if idx is not None:\n", + " arr[i, :] = data_up.values[idx, :]\n", + " return arr\n", + "\n", + "\n", + "def get_profile_indices(distance_1d):\n", + " \"\"\"\n", + " Returns regular distance bins and profile indices for CTD transect plotting.\n", + "\n", + " Bin size is set to one order of magnitude lower than max distance.\n", + " \"\"\"\n", + " dist_min, dist_max = float(distance_1d.min()), float(distance_1d.max())\n", + " if dist_max > 1e6:\n", + " dist_step = 1e5\n", + " elif dist_max > 1e5:\n", + " dist_step = 1e4\n", + " elif dist_max > 1e4:\n", + " dist_step = 1e3\n", + " else:\n", + " dist_step = 1e2 # fallback for very short transects\n", + "\n", + " distance_regular = np.arange(dist_min, dist_max + dist_step, dist_step)\n", + " threshold = dist_step / 2\n", + " profile_indices = [\n", + " np.argmin(np.abs(distance_1d.values - d))\n", + " if np.min(np.abs(distance_1d.values - d)) < threshold\n", + " else None\n", + " for d in distance_regular\n", + " ]\n", + " return profile_indices, distance_regular" + ] + }, + { + "cell_type": "markdown", + "id": "2bdf98e6", + "metadata": {}, + "source": [ + "Now we will execute the utility functions, plus define some extra useful arrays to be used for the plotting..." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "f59824a1", + "metadata": {}, + "outputs": [], + "source": [ + "# add distance from start\n", + "ctd_distance = distance_from_start(ctd_ds)\n", + "\n", + "# exract descent-only data\n", + "z_up = descent_only(ctd_distance, \"z\")\n", + "d_up = descent_only(ctd_distance, \"distance\")\n", + "var_up = descent_only(ctd_distance, VARIABLES[plot_variable][\"ds_name\"])\n", + "\n", + "# 1d array of depth dimension (from deepest trajectory)\n", + "traj_idx, obs_idx = np.where(z_up == np.nanmin(z_up))\n", + "z1d = z_up.values[traj_idx[0], :]\n", + "\n", + "# distance as 1d array\n", + "distance_1d = d_up.isel(obs=0)" + ] + }, + { + "cell_type": "markdown", + "id": "17745cf1", + "metadata": {}, + "source": [ + "## Plotting\n", + "\n", + "
\n", + "Note: The plots produced next are a starting point for your analysis. You are encouraged to make adjustments, for example axis limits and scaling if the defaults not best suited to your specific data. Use your preferred AI coding assistant for help!\n", + "
\n", + "\n", + "We are now ready to plot our transect data. We will use distance from the first waypoint/CTD cast for the x-axis, and water column depth for the y-axis. The data for the chosen variable will then be plotted according to the colour map. The CTD casts are likely to be different depths because some parts of the ocean are of course shallower than others.\n", + "\n", + "There are a few extra steps below which arrange the CTD casts into regular distance bins, so as to clearly demonstrate where along the transect we made CTD casts and indeed where there are gaps.\n", + "\n", + "
\n", + "Tip: Press the \"Expand Image\" button in the top right of the resultant plot to explore the plot in greater detail in a separate tab. Or the \"Save As\" button to save a copy of the image.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce83c3b9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# regularised transect\n", + "profile_indices, distance_regular = get_profile_indices(distance_1d)\n", + "var_masked = build_masked_array(var_up, profile_indices, len(distance_regular))\n", + "\n", + "xticks_reg = np.linspace(\n", + " float(distance_regular.min()),\n", + " float(distance_regular.max()),\n", + " len(distance_regular),\n", + ")\n", + "\n", + "# plot regularised transect\n", + "fig, ax = plt.subplots(figsize=(10, 6), dpi=90)\n", + "\n", + "ax.grid(True, which=\"both\", color=\"lightgrey\", linestyle=\"-\", linewidth=0.7, alpha=0.5)\n", + "\n", + "mesh = ax.pcolormesh(\n", + " distance_regular / 1000, # distance in km\n", + " z1d,\n", + " var_masked.T,\n", + " cmap=VARIABLES[plot_variable][\"cmap\"],\n", + ")\n", + "\n", + "ax.set_ylabel(\"Depth (m)\")\n", + "ax.set_xlabel(\"Distance from start (km)\")\n", + "\n", + "plt.colorbar(mesh, ax=ax, label=VARIABLES[plot_variable][\"label\"])\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "68c5c8c2", + "metadata": {}, + "source": [ + "In the plot above, we can see that there are gaps in the transects where no CTD casts have been made. After all, it's impossible to take measurements at every point across the transect! There will always be gaps making 10s of deployments across transects 1000s of kms long 🙃 This makes expedition/sampling site planning all the more important...\n", + "\n", + "We can also also plot a 'filled' version without the distance bins, to give an alternative view of the evolution across the transect which is not dominated by gaps and white space. This time we will also add a 'sea bed' to the plot.\n", + "\n", + "
\n", + "NOTE: It is important to always remember that the gaps do actually exist in reality and this is a caveat which must be considered when interpreting the transect derived from CTD casts. Indeed, if you look at the x-axis of the plot below you will see that the deployments are not necessarily regularly spaced and some gaps are larger than others.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fcf8a137", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot 'filled' transect (with sea bed visualised as well)\n", + "fig, ax = plt.subplots(figsize=(10, 6), dpi=96)\n", + "\n", + "mesh = ax.pcolormesh(\n", + " distance_1d / 1000, # distance in km\n", + " z1d,\n", + " var_up.T,\n", + " cmap=VARIABLES[plot_variable][\"cmap\"],\n", + ")\n", + "\n", + "seabed = xr.where(np.isnan(var_up), 1, np.nan) # sea bed\n", + "ax.pcolormesh(\n", + " distance_1d / 1000, # distance in km\n", + " z1d,\n", + " seabed.T,\n", + " cmap=mcolors.ListedColormap([mcolors.to_rgba(\"tan\"), mcolors.to_rgba(\"white\")]),\n", + ")\n", + "\n", + "tan_patch = mpatches.Patch(color=mcolors.to_rgba(\"tan\"), label=\"Land / sea bed\")\n", + "ax.legend(handles=[tan_patch], loc=\"lower right\")\n", + "\n", + "ax.set_xticks(distance_1d / 1000)\n", + "\n", + "ax.set_ylabel(\"Depth (m)\")\n", + "ax.set_xlabel(\"Distance from start (km)\")\n", + "\n", + "plt.colorbar(mesh, ax=ax, label=VARIABLES[plot_variable][\"label\"])\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97e62cec", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 52b813890f4cc6a4989757d6cd3441adf35597a0 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Mon, 1 Sep 2025 17:20:34 +0200 Subject: [PATCH 2/9] ADCP transect plot sample script --- .../user-guide/tutorials/ADCP_transects.ipynb | 322 ++++++++++++++++++ 1 file changed, 322 insertions(+) create mode 100644 docs/user-guide/tutorials/ADCP_transects.ipynb diff --git a/docs/user-guide/tutorials/ADCP_transects.ipynb b/docs/user-guide/tutorials/ADCP_transects.ipynb new file mode 100644 index 00000000..8bd8744f --- /dev/null +++ b/docs/user-guide/tutorials/ADCP_transects.ipynb @@ -0,0 +1,322 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bad21046", + "metadata": {}, + "source": [ + "# ADCP Transect Plotting\n", + "\n", + "This notebook demonstrates a simple plotting exercise for ADCP data across a transect, using the output of a VirtualShip expedition. There are example plots embedded at the end, but these will ultimately be replaced by your own versions as you work through the notebook.\n", + "\n", + "The plot(s) we will produce are simple plots which follow the trajectory of the expedition as a function of distance from the start, and are intended to be a starting point for your analysis. Because the `ADCP` instrument is an underway/onboard instrument, this means we benefit from continous recordings across the length of the ship's track (unlike overboard instruments such as CTDs which have to deployed at individual sampling sites).\n", + "\n", + "
\n", + "NOTE: This notebook assumes that each point along the expedition track is further from the start than the previous point. The code will still work if not, but the resultant plots might not be very intuitive.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "233041ec", + "metadata": {}, + "source": [ + "## Set up\n", + "\n", + "The first step is to import the Python packages required for post-processing the data and plotting. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f6c87472", + "metadata": {}, + "outputs": [], + "source": [ + "import cmocean.cm as cmo\n", + "import matplotlib.colors as mcolors\n", + "import matplotlib.patches as mpatches\n", + "import numpy as np\n", + "import xarray as xr\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "360f84ce", + "metadata": {}, + "source": [ + "Next, you should set `data_dir` to be the path to your expedition results in the code block below. You should replace `\"/path/to/EXPEDITION/results/\"` with the path for your machine.\n", + "\n", + "
\n", + "Tip: You can get the path to your expedition results by navigating to to the folder in Terminal (using `cd`) and then using the `pwd` command. This will print your working directory which you can copy to the `data_dir` variable in this notebook. Don't forget to keep it as a string (in \"quotation\" marks)!\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0cb630f6", + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = \"/path/to/EXPEDITION/results/\" # set this to be where your expedition output data is located on your (virtual) machine\n", + "data_dir = \"/Users/Atkin004/Documents/test_expeditions/sept_course/NORTH/results/\"" + ] + }, + { + "cell_type": "markdown", + "id": "d5930b00", + "metadata": {}, + "source": [ + "## Load data\n", + "\n", + "We are now ready to read in the data. You can carry on executing the next cells without making changes to the code..." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "654fb036", + "metadata": {}, + "outputs": [], + "source": [ + "# load ADCP data\n", + "adcp_ds = xr.open_dataset(f\"{data_dir}/adcp.zarr\")\n", + "if adcp_ds[\"obs\"].size <= 1:\n", + " raise ValueError(\"Number of waypoints must be > 1\")" + ] + }, + { + "cell_type": "markdown", + "id": "1221167e", + "metadata": {}, + "source": [ + "## Data post-processing\n", + "\n", + "Before we can continue, we need to do some post-processing to get it ready for plotting. Below are various helper functions which perform tasks such as calculating the ship's distance from the start of the transect at each point and calculating the various velocity components from the ADCP data." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0aa1f8f4", + "metadata": {}, + "outputs": [], + "source": [ + "# utility functions\n", + "\n", + "\n", + "def haversine(lon1, lat1, lon2, lat2):\n", + " \"\"\"Great-circle distance (meters) between two points.\"\"\"\n", + " lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n", + " dlon, dlat = lon2 - lon1, lat2 - lat1\n", + " a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2\n", + " c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))\n", + " return 6371000 * c\n", + "\n", + "\n", + "def distance_from_start(ds):\n", + " \"\"\"Array of meters from first waypoint.\"\"\"\n", + " lon0, lat0 = ds.isel(obs=0)[\"lon\"].values, ds.isel(obs=0)[\"lat\"].values\n", + " d = np.zeros_like(ds[\"lon\"].values, dtype=float)\n", + " for ob, (lon, lat) in enumerate(zip(ds[\"lon\"], ds[\"lat\"], strict=False)):\n", + " d[ob] = haversine(lon, lat, lon0, lat0)\n", + " return d\n", + "\n", + "\n", + "def calc_velocities(ds):\n", + " \"\"\"Calculate absolute, parallel and perpendicular (to the ship trajectory) velocities, as well as (compass) direction of flow.\"\"\"\n", + " Uabs = np.sqrt(ds[\"U\"] ** 2 + ds[\"V\"] ** 2)\n", + " ds_surface = ds.isel(trajectory=0)\n", + " dlon = np.deg2rad(ds_surface[\"lon\"].differentiate(\"obs\"))\n", + " dlat = np.deg2rad(ds_surface[\"lat\"].differentiate(\"obs\"))\n", + " lat = np.deg2rad(ds_surface[\"lat\"])\n", + " alpha = np.arctan(dlat / (dlon * np.cos(lat))).mean(\"obs\") # cruise direction angle\n", + " Uparallel = np.cos(alpha) * ds[\"U\"] + np.sin(alpha) * ds[\"V\"] # cross-shore vel\n", + " Uperp = -np.sin(alpha) * ds[\"U\"] + np.cos(alpha) * ds[\"V\"] # long-shore vel\n", + " direction_rad = np.arctan2(\n", + " ds[\"U\"], ds[\"V\"]\n", + " ) # direction of flow [degrees from north]\n", + " direction_deg = (np.degrees(direction_rad) + 360) % 360\n", + "\n", + " return Uabs, Uparallel, Uperp, direction_deg" + ] + }, + { + "cell_type": "markdown", + "id": "88062e97", + "metadata": {}, + "source": [ + "Now we will execute the various post-processing calculations, plus define some extra useful arrays to be used for the plotting..." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6433742a", + "metadata": {}, + "outputs": [], + "source": [ + "# distance from start as 1d array\n", + "distance_1d = distance_from_start(adcp_ds.isel(trajectory=0))\n", + "\n", + "# calculate velocity components and direction\n", + "Uabs, Uparallel, Uperp, direction = calc_velocities(adcp_ds)\n", + "\n", + "# land / sea bed mask\n", + "landmask = xr.where(((adcp_ds[\"U\"] == 0) & (adcp_ds[\"V\"] == 0)), 1, np.nan)" + ] + }, + { + "cell_type": "markdown", + "id": "cd613a45", + "metadata": {}, + "source": [ + "## Plotting\n", + "\n", + "
\n", + "Note: The plots produced next are a starting point for your analysis. You are encouraged to make adjustments, for example axis limits and scaling if the defaults not best suited to your specific data. Use your preferred AI coding assistant for help!\n", + "
\n", + "\n", + "We are now ready to plot our transect data. We will use distance from the start of the transect/expedition for the x-axis, and water column depth for the y-axis. The ADCP data will then be plotted according to the colour map for diagnostic. The profiles across the transect are likely to be different depths because some parts of the ocean are of course shallower than others.\n", + "\n", + "
\n", + "Tip: Press the \"Expand Image\" button in the top right of the resultant plot to explore the plot in greater detail in a separate tab. Or the \"Save As\" button to save a copy of the image.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93693258", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PLOT_DICT = {\n", + " \"Uabs\": {\n", + " \"data\": Uabs,\n", + " \"cmap\": cmo.speed,\n", + " \"label\": \"Absolute velocity\",\n", + " },\n", + " \"Uparallel\": {\n", + " \"data\": Uparallel,\n", + " \"cmap\": cmo.balance,\n", + " \"label\": \"Along-track velocity (positive with flow)\",\n", + " },\n", + " \"Uperp\": {\n", + " \"data\": Uperp,\n", + " \"cmap\": cmo.balance,\n", + " \"label\": \"Cross-track velocity (positive with flow to the left)\",\n", + " },\n", + " \"direction\": {\n", + " \"data\": direction,\n", + " \"cmap\": cmo.phase,\n", + " \"label\": \"Flow direction (0=N, 90=E, 180=S, 270=W)\",\n", + " },\n", + "}\n", + "\n", + "# fig\n", + "fig, axs = plt.subplots(\n", + " len(PLOT_DICT), 1, figsize=(10, 10), dpi=96, sharex=True, sharey=True\n", + ")\n", + "\n", + "norm = mcolors.CenteredNorm()\n", + "\n", + "for idx, ((key, var), ax) in enumerate(zip(PLOT_DICT.items(), axs, strict=False)):\n", + " # adcp data\n", + " mesh = ax.pcolormesh(\n", + " distance_1d / 1000,\n", + " adcp_ds[\"z\"],\n", + " var[\"data\"],\n", + " cmap=var[\"cmap\"],\n", + " )\n", + "\n", + " # seabed\n", + " ax.pcolormesh(\n", + " distance_1d / 1000, # distance in km\n", + " adcp_ds[\"z\"],\n", + " landmask,\n", + " cmap=mcolors.ListedColormap([mcolors.to_rgba(\"tan\"), mcolors.to_rgba(\"white\")]),\n", + " )\n", + "\n", + " # title\n", + " ax.set_title(var[\"label\"])\n", + "\n", + " # colorbar\n", + " if key == \"direction\":\n", + " cbar = fig.colorbar(\n", + " mesh,\n", + " ax=ax,\n", + " label=\"Degrees from North\",\n", + " ticks=[0, 90, 180, 270],\n", + " )\n", + " else:\n", + " cbar = fig.colorbar(\n", + " mesh,\n", + " ax=ax,\n", + " label=r\"m s$^{-1}$\",\n", + " )\n", + "\n", + " # axis labels\n", + " ax.set_ylabel(\"Depth (m)\")\n", + " if idx == len(axs) - 1: # bottom panel only for single column of subplots\n", + " ax.set_xlabel(\"Distance from start (km)\")\n", + "\n", + "# legend for sea bed\n", + "tan_patch = mpatches.Patch(color=mcolors.to_rgba(\"tan\"), label=\"Land / sea bed\")\n", + "axs[-1].legend(handles=[tan_patch], loc=\"lower right\")\n", + "\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "f2ada2b1", + "metadata": {}, + "source": [ + "The resultant figure shows three components of velocity recorded by the ADCP instrument.\n", + "\n", + "1) Absolute velocity\n", + "2) Along-track velocity (where positive values indicate flow in the direction of the ship's track across the the transect)\n", + "3) Cross-track velocity (where postive values indicate flow to the left of the ship's direction).\n", + "4) The direction of the flow, expressed as degrees from North.\n", + "\n", + "You can use these plots as a starting point to consider how flow dynamics vary over the cross-section. You may find some diagnostics more useful than others, depending on your specific aims!" + ] + } + ], + "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": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From cdfb7776d00f5a9a0b73bbc936b2459de70724c4 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Mon, 1 Sep 2025 17:43:17 +0200 Subject: [PATCH 3/9] update index to include new tutorials --- docs/user-guide/tutorials/index.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/user-guide/tutorials/index.md b/docs/user-guide/tutorials/index.md index 339974ff..9b181aff 100644 --- a/docs/user-guide/tutorials/index.md +++ b/docs/user-guide/tutorials/index.md @@ -10,4 +10,6 @@ ADCP_data_tutorial.ipynb CTD_data_tutorial.ipynb Drifter_data_tutorial.ipynb Argo_data_tutorial.ipynb +CTD_transects.ipynb +ADCP_transects.ipynb ``` From 7f850897ea5370824ec4e399ea4e480cc3725d3d Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 2 Sep 2025 09:31:12 +0200 Subject: [PATCH 4/9] proof reading changes --- .../user-guide/tutorials/ADCP_transects.ipynb | 15 ++++++++---- docs/user-guide/tutorials/CTD_transects.ipynb | 23 ++++++++++--------- 2 files changed, 22 insertions(+), 16 deletions(-) diff --git a/docs/user-guide/tutorials/ADCP_transects.ipynb b/docs/user-guide/tutorials/ADCP_transects.ipynb index 8bd8744f..b0cf4d08 100644 --- a/docs/user-guide/tutorials/ADCP_transects.ipynb +++ b/docs/user-guide/tutorials/ADCP_transects.ipynb @@ -9,7 +9,7 @@ "\n", "This notebook demonstrates a simple plotting exercise for ADCP data across a transect, using the output of a VirtualShip expedition. There are example plots embedded at the end, but these will ultimately be replaced by your own versions as you work through the notebook.\n", "\n", - "The plot(s) we will produce are simple plots which follow the trajectory of the expedition as a function of distance from the start, and are intended to be a starting point for your analysis. Because the `ADCP` instrument is an underway/onboard instrument, this means we benefit from continous recordings across the length of the ship's track (unlike overboard instruments such as CTDs which have to deployed at individual sampling sites).\n", + "The plot(s) we will produce are simple plots which follow the trajectory of the expedition as a function of distance from the start, and are intended to be a starting point for your analysis. Because the `ADCP` instrument is an underway/onboard instrument, this means we benefit from continuous recordings across the length of the ship's track (unlike overboard instruments such as CTDs which have to deployed at individual sampling sites).\n", "\n", "
\n", "NOTE: This notebook assumes that each point along the expedition track is further from the start than the previous point. The code will still work if not, but the resultant plots might not be very intuitive.\n", @@ -23,6 +23,8 @@ "source": [ "## Set up\n", "\n", + "#### Imports\n", + "\n", "The first step is to import the Python packages required for post-processing the data and plotting. " ] }, @@ -46,6 +48,9 @@ "id": "360f84ce", "metadata": {}, "source": [ + "\n", + "#### Data directory\n", + "\n", "Next, you should set `data_dir` to be the path to your expedition results in the code block below. You should replace `\"/path/to/EXPEDITION/results/\"` with the path for your machine.\n", "\n", "
\n", @@ -55,13 +60,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "id": "0cb630f6", "metadata": {}, "outputs": [], "source": [ - "data_dir = \"/path/to/EXPEDITION/results/\" # set this to be where your expedition output data is located on your (virtual) machine\n", - "data_dir = \"/Users/Atkin004/Documents/test_expeditions/sept_course/NORTH/results/\"" + "data_dir = \"/path/to/EXPEDITION/results/\" # set this to be where your expedition output data is located on your (virtual) machine" ] }, { @@ -148,6 +152,7 @@ "id": "88062e97", "metadata": {}, "source": [ + "\n", "Now we will execute the various post-processing calculations, plus define some extra useful arrays to be used for the plotting..." ] }, @@ -287,7 +292,7 @@ "id": "f2ada2b1", "metadata": {}, "source": [ - "The resultant figure shows three components of velocity recorded by the ADCP instrument.\n", + "The resultant figure shows various components of the velocity field, derived from ADCP data.\n", "\n", "1) Absolute velocity\n", "2) Along-track velocity (where positive values indicate flow in the direction of the ship's track across the the transect)\n", diff --git a/docs/user-guide/tutorials/CTD_transects.ipynb b/docs/user-guide/tutorials/CTD_transects.ipynb index 1f168e78..57748382 100644 --- a/docs/user-guide/tutorials/CTD_transects.ipynb +++ b/docs/user-guide/tutorials/CTD_transects.ipynb @@ -14,7 +14,7 @@ "The plot(s) we will produce are simple plots which follow the trajectory of the expedition as a function of distance from the first waypoint, and are intended to be a starting point for your analysis. \n", "\n", "
\n", - "NOTE: This notebook assumes that each waypoint in the expedition is further from the start than the last waypoint. The code will still work if not, but the resultant plots might not be very intuitive.\n", + "Note: This notebook assumes that each waypoint in the expedition is further from the start than the last waypoint. The code will still work if not, but the resultant plots might not be very intuitive.\n", "
" ] }, @@ -25,6 +25,8 @@ "source": [ "## Set up\n", "\n", + "#### Imports\n", + "\n", "The first step is to import the Python packages required for post-processing the data and plotting. " ] }, @@ -48,6 +50,9 @@ "id": "4f387780", "metadata": {}, "source": [ + "\n", + "#### Data directory\n", + "\n", "Next, you should set `data_dir` to be the path to your expedition results in the code block below. You should replace `\"/path/to/EXPEDITION/results/\"` with the path for your machine.\n", "\n", "
\n", @@ -70,6 +75,8 @@ "id": "a499ebe2", "metadata": {}, "source": [ + "#### Variable choice\n", + "\n", "You should now consider which variable from your CTD casts you would like to plot. Which ones are available to you will depend on whether you have used the `CTD` (physical variables) or `CTD_BGC` (biogeochemical) instrument, or both. Below is a list of all valid variable choices for both instruments...\n", "\n", "`CTD` (physical):\n", @@ -104,7 +111,8 @@ "id": "a05fad14", "metadata": {}, "source": [ - "We also define the `VARIABLES` dictionary here, which we use to store some parameters for the plots (e.g. variable labels, what units each is in, and which colour map we should use for the plots).\n", + "\n", + "We also define the `VARIABLES` dictionary here, which we use to store some parameters for the plots related to each variable choice (e.g. labels, what units each is in, and which colour map we should use for the plots).\n", "\n", "
\n", "Tip: You don't need to change anything here, but should you wish to change the colour scheme (`cmap`) for any CTD variable you can do so. At the moment it's set to use relevant cmaps from the cmocean Python package, which has developed specialist colour schemes for oceanographic data applications.\n", @@ -306,6 +314,7 @@ "id": "2bdf98e6", "metadata": {}, "source": [ + "\n", "Now we will execute the utility functions, plus define some extra useful arrays to be used for the plotting..." ] }, @@ -409,7 +418,7 @@ "We can also also plot a 'filled' version without the distance bins, to give an alternative view of the evolution across the transect which is not dominated by gaps and white space. This time we will also add a 'sea bed' to the plot.\n", "\n", "
\n", - "NOTE: It is important to always remember that the gaps do actually exist in reality and this is a caveat which must be considered when interpreting the transect derived from CTD casts. Indeed, if you look at the x-axis of the plot below you will see that the deployments are not necessarily regularly spaced and some gaps are larger than others.\n", + "Note: It is important to remember that the gaps do actually exist in reality and this is a caveat which must be considered when interpreting the transect derived from CTD casts. Indeed, if you look at the x-axis of the plot below you will see that the deployments are not necessarily regularly spaced and some gaps are larger than others.\n", "
" ] }, @@ -460,14 +469,6 @@ "plt.colorbar(mesh, ax=ax, label=VARIABLES[plot_variable][\"label\"])\n", "plt.tight_layout()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "97e62cec", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 91e8f59fe23bf3f09870f37b81ed8cfdf1e15e1c Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 2 Sep 2025 10:03:45 +0200 Subject: [PATCH 5/9] more proof reading --- .../user-guide/tutorials/ADCP_transects.ipynb | 7 +++--- docs/user-guide/tutorials/CTD_transects.ipynb | 24 +++++++++---------- 2 files changed, 16 insertions(+), 15 deletions(-) diff --git a/docs/user-guide/tutorials/ADCP_transects.ipynb b/docs/user-guide/tutorials/ADCP_transects.ipynb index b0cf4d08..35faa881 100644 --- a/docs/user-guide/tutorials/ADCP_transects.ipynb +++ b/docs/user-guide/tutorials/ADCP_transects.ipynb @@ -12,7 +12,7 @@ "The plot(s) we will produce are simple plots which follow the trajectory of the expedition as a function of distance from the start, and are intended to be a starting point for your analysis. Because the `ADCP` instrument is an underway/onboard instrument, this means we benefit from continuous recordings across the length of the ship's track (unlike overboard instruments such as CTDs which have to deployed at individual sampling sites).\n", "\n", "
\n", - "NOTE: This notebook assumes that each point along the expedition track is further from the start than the previous point. The code will still work if not, but the resultant plots might not be very intuitive.\n", + "Note: This notebook assumes that each point along the expedition track is further from the start than the previous point. The code will still work if not, but the resultant plots might not be very intuitive.\n", "
" ] }, @@ -55,12 +55,13 @@ "\n", "
\n", "Tip: You can get the path to your expedition results by navigating to to the folder in Terminal (using `cd`) and then using the `pwd` command. This will print your working directory which you can copy to the `data_dir` variable in this notebook. Don't forget to keep it as a string (in \"quotation\" marks)!\n", - "
" + "
\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "id": "0cb630f6", "metadata": {}, "outputs": [], diff --git a/docs/user-guide/tutorials/CTD_transects.ipynb b/docs/user-guide/tutorials/CTD_transects.ipynb index 57748382..61a109a1 100644 --- a/docs/user-guide/tutorials/CTD_transects.ipynb +++ b/docs/user-guide/tutorials/CTD_transects.ipynb @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 74, "id": "8de8b4ae", "metadata": {}, "outputs": [], @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 75, "id": "b32d2730", "metadata": {}, "outputs": [], @@ -139,17 +139,17 @@ " },\n", " \"oxygen\": {\n", " \"cmap\": cmo.oxy,\n", - " \"label\": \"Dissolved oxygen (mmol m-3)\",\n", + " \"label\": r\"Dissolved oxygen (mmol m$^{-3}$)\",\n", " \"ds_name\": \"o2\",\n", " },\n", " \"nitrate\": {\n", " \"cmap\": cmo.matter,\n", - " \"label\": \"Nitrate (mmol m-3)\",\n", + " \"label\": r\"Nitrate (mmol m$^{-3}$)\",\n", " \"ds_name\": \"no3\",\n", " },\n", " \"phosphate\": {\n", " \"cmap\": cmo.matter,\n", - " \"label\": \"Phosphate (mmol m-3)\",\n", + " \"label\": r\"Phosphate (mmol m$^{-3}$)\",\n", " \"ds_name\": \"po4\",\n", " },\n", " \"ph\": {\n", @@ -159,22 +159,22 @@ " },\n", " \"zooplankton\": {\n", " \"cmap\": cmo.algae,\n", - " \"label\": \"Total zooplankton (mmol m-3)\",\n", + " \"label\": r\"Total zooplankton (mmol m$^{-3}$)\",\n", " \"ds_name\": \"zooc\",\n", " },\n", " \"phytoplankton\": {\n", " \"cmap\": cmo.algae,\n", - " \"label\": \"Total phytoplankton (mmol m-3)\",\n", + " \"label\": r\"Total phytoplankton (mmol m$^{-3}$)\",\n", " \"ds_name\": \"phyc\",\n", " },\n", " \"primary_production\": {\n", " \"cmap\": cmo.matter,\n", - " \"label\": \"Total primary production of phytoplankton (mg m-3 day-1)\",\n", + " \"label\": r\"Total primary production of phytoplankton (mg m$^{-3}$ day$^{-1}$)\",\n", " \"ds_name\": \"nppv\",\n", " },\n", " \"chlorophyll\": {\n", " \"cmap\": cmo.algae,\n", - " \"label\": \"Chlorophyll (mg m-3)\",\n", + " \"label\": r\"Chlorophyll (mg m$^{-3}$)\",\n", " \"ds_name\": \"chl\",\n", " },\n", "}" @@ -192,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 76, "id": "13f4664b", "metadata": {}, "outputs": [], @@ -218,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 77, "id": "785b2b35", "metadata": {}, "outputs": [], @@ -320,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 78, "id": "f59824a1", "metadata": {}, "outputs": [], From d5b0a40e8ef3c978f380c875c44d9069ded37abb Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 2 Sep 2025 12:14:54 +0200 Subject: [PATCH 6/9] retry docs build From c7ff4059d3b87b6769552348753f4aae41f76821 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 2 Sep 2025 15:40:04 +0200 Subject: [PATCH 7/9] small edits to suit different setups --- docs/user-guide/tutorials/ADCP_transects.ipynb | 16 ++++++++-------- docs/user-guide/tutorials/CTD_transects.ipynb | 17 +++++++++-------- 2 files changed, 17 insertions(+), 16 deletions(-) diff --git a/docs/user-guide/tutorials/ADCP_transects.ipynb b/docs/user-guide/tutorials/ADCP_transects.ipynb index 35faa881..3fbb0a46 100644 --- a/docs/user-guide/tutorials/ADCP_transects.ipynb +++ b/docs/user-guide/tutorials/ADCP_transects.ipynb @@ -25,7 +25,11 @@ "\n", "#### Imports\n", "\n", - "The first step is to import the Python packages required for post-processing the data and plotting. " + "The first step is to import the Python packages required for post-processing the data and plotting. \n", + "\n", + "
\n", + "Tip: You may need to set the Kernel to the relevant (Conda) environment in the top right of this notebook to access the required packages! \n", + "
\n" ] }, { @@ -54,7 +58,7 @@ "Next, you should set `data_dir` to be the path to your expedition results in the code block below. You should replace `\"/path/to/EXPEDITION/results/\"` with the path for your machine.\n", "\n", "
\n", - "Tip: You can get the path to your expedition results by navigating to to the folder in Terminal (using `cd`) and then using the `pwd` command. This will print your working directory which you can copy to the `data_dir` variable in this notebook. Don't forget to keep it as a string (in \"quotation\" marks)!\n", + "Tip: You can get the path to your expedition results by navigating to the `results` folder in Terminal (using `cd`) and then using the `pwd` command. This will print your working directory which you can copy to the `data_dir` variable in this notebook. Don't forget to keep it as a string (in \"quotation\" marks)!\n", "
\n", "\n" ] @@ -104,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "0aa1f8f4", "metadata": {}, "outputs": [], @@ -185,11 +189,7 @@ "Note: The plots produced next are a starting point for your analysis. You are encouraged to make adjustments, for example axis limits and scaling if the defaults not best suited to your specific data. Use your preferred AI coding assistant for help!\n", "
\n", "\n", - "We are now ready to plot our transect data. We will use distance from the start of the transect/expedition for the x-axis, and water column depth for the y-axis. The ADCP data will then be plotted according to the colour map for diagnostic. The profiles across the transect are likely to be different depths because some parts of the ocean are of course shallower than others.\n", - "\n", - "
\n", - "Tip: Press the \"Expand Image\" button in the top right of the resultant plot to explore the plot in greater detail in a separate tab. Or the \"Save As\" button to save a copy of the image.\n", - "
" + "We are now ready to plot our transect data. We will use distance from the start of the transect/expedition for the x-axis, and water column depth for the y-axis. The ADCP data will then be plotted according to the colour map for diagnostic. The profiles across the transect are likely to be different depths because some parts of the ocean are of course shallower than others." ] }, { diff --git a/docs/user-guide/tutorials/CTD_transects.ipynb b/docs/user-guide/tutorials/CTD_transects.ipynb index 61a109a1..63d8ad00 100644 --- a/docs/user-guide/tutorials/CTD_transects.ipynb +++ b/docs/user-guide/tutorials/CTD_transects.ipynb @@ -27,7 +27,11 @@ "\n", "#### Imports\n", "\n", - "The first step is to import the Python packages required for post-processing the data and plotting. " + "The first step is to import the Python packages required for post-processing the data and plotting. \n", + "\n", + "
\n", + "Tip: You may need to set the Kernel to the relevant (Conda) environment in the top right of this notebook to access the required packages! \n", + "
" ] }, { @@ -56,8 +60,9 @@ "Next, you should set `data_dir` to be the path to your expedition results in the code block below. You should replace `\"/path/to/EXPEDITION/results/\"` with the path for your machine.\n", "\n", "
\n", - "Tip: You can get the path to your expedition results by navigating to to the folder in Terminal (using `cd`) and then using the `pwd` command. This will print your working directory which you can copy to the `data_dir` variable in this notebook. Don't forget to keep it as a string (in \"quotation\" marks)!\n", - "
" + "Tip: You can get the path to your expedition results by navigating to the `results` folder in Terminal (using `cd`) and then using the `pwd` command. This will print your working directory which you can copy to the `data_dir` variable in this notebook. Don't forget to keep it as a string (in \"quotation\" marks)!\n", + "
\n", + "\n" ] }, { @@ -354,11 +359,7 @@ "\n", "We are now ready to plot our transect data. We will use distance from the first waypoint/CTD cast for the x-axis, and water column depth for the y-axis. The data for the chosen variable will then be plotted according to the colour map. The CTD casts are likely to be different depths because some parts of the ocean are of course shallower than others.\n", "\n", - "There are a few extra steps below which arrange the CTD casts into regular distance bins, so as to clearly demonstrate where along the transect we made CTD casts and indeed where there are gaps.\n", - "\n", - "
\n", - "Tip: Press the \"Expand Image\" button in the top right of the resultant plot to explore the plot in greater detail in a separate tab. Or the \"Save As\" button to save a copy of the image.\n", - "
" + "There are a few extra steps below which arrange the CTD casts into regular distance bins, so as to clearly demonstrate where along the transect we made CTD casts and indeed where there are gaps.\n" ] }, { From d14f26ed6fd7ee5946224afbeedc82b6dd09d1eb Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 3 Sep 2025 14:42:54 +0200 Subject: [PATCH 8/9] correction to centreing velocity diagnostic cmaps around 0 --- docs/user-guide/tutorials/ADCP_transects.ipynb | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/docs/user-guide/tutorials/ADCP_transects.ipynb b/docs/user-guide/tutorials/ADCP_transects.ipynb index 3fbb0a46..19a243cb 100644 --- a/docs/user-guide/tutorials/ADCP_transects.ipynb +++ b/docs/user-guide/tutorials/ADCP_transects.ipynb @@ -194,13 +194,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "93693258", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -215,21 +215,25 @@ " \"data\": Uabs,\n", " \"cmap\": cmo.speed,\n", " \"label\": \"Absolute velocity\",\n", + " \"norm\": None,\n", " },\n", " \"Uparallel\": {\n", " \"data\": Uparallel,\n", " \"cmap\": cmo.balance,\n", " \"label\": \"Along-track velocity (positive with flow)\",\n", + " \"norm\": mcolors.CenteredNorm(vcenter=0),\n", " },\n", " \"Uperp\": {\n", " \"data\": Uperp,\n", " \"cmap\": cmo.balance,\n", " \"label\": \"Cross-track velocity (positive with flow to the left)\",\n", + " \"norm\": mcolors.CenteredNorm(vcenter=0),\n", " },\n", " \"direction\": {\n", " \"data\": direction,\n", " \"cmap\": cmo.phase,\n", " \"label\": \"Flow direction (0=N, 90=E, 180=S, 270=W)\",\n", + " \"norm\": None,\n", " },\n", "}\n", "\n", @@ -238,8 +242,6 @@ " len(PLOT_DICT), 1, figsize=(10, 10), dpi=96, sharex=True, sharey=True\n", ")\n", "\n", - "norm = mcolors.CenteredNorm()\n", - "\n", "for idx, ((key, var), ax) in enumerate(zip(PLOT_DICT.items(), axs, strict=False)):\n", " # adcp data\n", " mesh = ax.pcolormesh(\n", @@ -247,6 +249,7 @@ " adcp_ds[\"z\"],\n", " var[\"data\"],\n", " cmap=var[\"cmap\"],\n", + " norm=var[\"norm\"] if var[\"norm\"] is not None else None,\n", " )\n", "\n", " # seabed\n", From 45e9b011496ea8e916f8f5940a26c11465678b15 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 3 Sep 2025 15:02:59 +0200 Subject: [PATCH 9/9] more proof reading --- docs/user-guide/tutorials/ADCP_transects.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/user-guide/tutorials/ADCP_transects.ipynb b/docs/user-guide/tutorials/ADCP_transects.ipynb index 19a243cb..b35a916b 100644 --- a/docs/user-guide/tutorials/ADCP_transects.ipynb +++ b/docs/user-guide/tutorials/ADCP_transects.ipynb @@ -142,8 +142,8 @@ " dlat = np.deg2rad(ds_surface[\"lat\"].differentiate(\"obs\"))\n", " lat = np.deg2rad(ds_surface[\"lat\"])\n", " alpha = np.arctan(dlat / (dlon * np.cos(lat))).mean(\"obs\") # cruise direction angle\n", - " Uparallel = np.cos(alpha) * ds[\"U\"] + np.sin(alpha) * ds[\"V\"] # cross-shore vel\n", - " Uperp = -np.sin(alpha) * ds[\"U\"] + np.cos(alpha) * ds[\"V\"] # long-shore vel\n", + " Uparallel = np.cos(alpha) * ds[\"U\"] + np.sin(alpha) * ds[\"V\"]\n", + " Uperp = -np.sin(alpha) * ds[\"U\"] + np.cos(alpha) * ds[\"V\"]\n", " direction_rad = np.arctan2(\n", " ds[\"U\"], ds[\"V\"]\n", " ) # direction of flow [degrees from north]\n", @@ -194,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "93693258", "metadata": {}, "outputs": [ @@ -299,7 +299,7 @@ "The resultant figure shows various components of the velocity field, derived from ADCP data.\n", "\n", "1) Absolute velocity\n", - "2) Along-track velocity (where positive values indicate flow in the direction of the ship's track across the the transect)\n", + "2) Along-track velocity (where positive values indicate flow in the overall direction of the ship's track across the the transect)\n", "3) Cross-track velocity (where postive values indicate flow to the left of the ship's direction).\n", "4) The direction of the flow, expressed as degrees from North.\n", "\n",