{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Reading ADCP Data with MHKiT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following example demonstrates a simple workflow for analyzing Acoustic Doppler Current Profiler (ADCP) data using MHKiT. MHKiT has brought the DOLfYN codebase in as an MHKiT module for working with ADCP and Acoustic Doppler Velocimeter (ADV) data.\n", "\n", "A typical ADCP data workflow is broken down into\n", " 1. Review the raw data\n", " - Check timestamps\n", " - Calculate/check that the depth bin locations are correct\n", " - Look at velocity, beam amplitude and/or beam correlation data quality\n", " 2. Remove data located above the water surface or below the seafloor\n", " 3. Check for spurious datapoints and remove if necessary\n", " 4. If not already done within the instrument, average the data into time bins of a set time length (normally 5 to 10 min)\n", " 5. Conduct further analysis as required\n", "\n", "Start by importing the necessary tools:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [], "source": [ "from mhkit import dolfyn\n", "from mhkit.dolfyn.adp import api" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read Raw Instrument Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The core benefit of DOLfYN is that it can read in raw data directly after transferring it off of the ADCP. The ADCP used here is a Nortek Signature1000, with the file extension '.ad2cp'. This specific dataset contains several hours worth of velocity data collected at 1 Hz from the ADCP mounted on a bottom lander in a tidal inlet. \n", "The instruments that DOLfYN supports are listed in the [docs](https://mhkit-software.github.io/MHKiT/mhkit-python/api.dolfyn.html).\n", "\n", "Start by reading in the raw datafile downloaded from the instrument. The `read` function reads the raw file and dumps the information into an xarray Dataset, which contains a few groups of variables:\n", "\n", "1. Velocity in the instrument-saved coordinate system (beam, XYZ, ENU)\n", "2. Beam amplitude and correlation data\n", "3. Measurements of the instrument's bearing and environment\n", "4. Orientation matrices DOLfYN uses for rotating through coordinate frames.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Indexing data/dolfyn/Sig1000_tidal.ad2cp... Done.\n", "Reading file data/dolfyn/Sig1000_tidal.ad2cp ...\n" ] } ], "source": [ "ds = dolfyn.read('data/dolfyn/Sig1000_tidal.ad2cp')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two ways to see what's in a Dataset. The first is to simply type the dataset's name to see the standard xarray output. To access a particular variable in a dataset, use dict-style (`ds['vel']`) or attribute-style syntax (`ds.vel`). See the [xarray docs](http://xarray.pydata.org/en/stable/getting-started-guide/quick-overview.html) for more details on how to use the xarray format." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
                            "Dimensions:              (time: 55000, time_b5: 55000, dirIMU: 3, range_b5: 28, beam: 4, range: 28, dir: 4, earth: 3, inst: 3, q: 4, x: 4, x*: 4)\n",
                            "Coordinates:\n",
                            "  * time                 (time) datetime64[ns] 2020-08-15T00:20:00.500999 ......\n",
                            "  * time_b5              (time_b5) datetime64[ns] 2020-08-15T00:20:00.438499 ...\n",
                            "  * dirIMU               (dirIMU) <U1 'E' 'N' 'U'\n",
                            "  * range_b5             (range_b5) float64 0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1\n",
                            "  * beam                 (beam) int32 1 2 3 4\n",
                            "  * range                (range) float64 0.6 1.1 1.6 2.1 ... 12.6 13.1 13.6 14.1\n",
                            "  * dir                  (dir) <U2 'E' 'N' 'U1' 'U2'\n",
                            "  * earth                (earth) <U1 'E' 'N' 'U'\n",
                            "  * inst                 (inst) <U1 'X' 'Y' 'Z'\n",
                            "  * q                    (q) <U1 'w' 'x' 'y' 'z'\n",
                            "  * x                    (x) int32 1 2 3 4\n",
                            "  * x*                   (x*) int32 1 2 3 4\n",
                            "Data variables: (12/38)\n",
                            "    c_sound              (time) float32 1.502e+03 1.502e+03 ... 1.498e+03\n",
                            "    temp                 (time) float32 14.55 14.55 14.55 ... 13.47 13.47 13.47\n",
                            "    pressure             (time) float32 9.713 9.718 9.718 ... 9.596 9.594 9.596\n",
                            "    mag                  (dirIMU, time) float32 72.5 72.7 72.6 ... -197.2 -195.7\n",
                            "    accel                (dirIMU, time) float32 -0.00479 -0.01437 ... 9.729\n",
                            "    batt                 (time) float32 16.6 16.6 16.6 16.6 ... 16.4 16.4 15.2\n",
                            "    ...                   ...\n",
                            "    telemetry_data       (time) uint8 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0\n",
                            "    boost_running        (time) uint8 0 0 0 0 0 0 0 0 1 0 ... 0 1 0 0 0 0 0 0 1\n",
                            "    heading              (time) float32 -12.52 -12.51 -12.51 ... -12.52 -12.5\n",
                            "    pitch                (time) float32 -0.065 -0.06 -0.06 ... -0.06 -0.05 -0.05\n",
                            "    roll                 (time) float32 -7.425 -7.42 -7.42 ... -6.45 -6.45 -6.45\n",
                            "    beam2inst_orientmat  (x, x*) float32 1.183 0.0 -1.183 ... 0.5518 0.0 0.5518\n",
                            "Attributes: (12/33)\n",
                            "    filehead_config:       {'CLOCKSTR': {'TIME': '"2020-08-13 13:56:21"'}, 'I...\n",
                            "    inst_model:            Signature1000\n",
                            "    inst_make:             Nortek\n",
                            "    inst_type:             ADCP\n",
                            "    rotate_vars:           ['vel', 'accel', 'accel_b5', 'angrt', 'angrt_b5', ...\n",
                            "    burst_config:          {'press_valid': True, 'temp_valid': True, 'compass...\n",
                            "    ...                    ...\n",
                            "    proc_idle_less_3pct:   0\n",
                            "    proc_idle_less_6pct:   0\n",
                            "    proc_idle_less_12pct:  0\n",
                            "    coord_sys:             earth\n",
                            "    has_imu:               1\n",
                            "    fs:                    1
" ], "text/plain": [ "\n", "Dimensions: (time: 55000, time_b5: 55000, dirIMU: 3, range_b5: 28, beam: 4, range: 28, dir: 4, earth: 3, inst: 3, q: 4, x: 4, x*: 4)\n", "Coordinates:\n", " * time (time) datetime64[ns] 2020-08-15T00:20:00.500999 ......\n", " * time_b5 (time_b5) datetime64[ns] 2020-08-15T00:20:00.438499 ...\n", " * dirIMU (dirIMU) : Nortek Signature1000\n", " . 15.28 hours (started: Aug 15, 2020 00:20)\n", " . earth-frame\n", " . (55000 pings @ 1Hz)\n", " Variables:\n", " - time ('time',)\n", " - time_b5 ('time_b5',)\n", " - vel ('dir', 'range', 'time')\n", " - vel_b5 ('range_b5', 'time_b5')\n", " - range ('range',)\n", " - orientmat ('earth', 'inst', 'time')\n", " - heading ('time',)\n", " - pitch ('time',)\n", " - roll ('time',)\n", " - temp ('time',)\n", " - pressure ('time',)\n", " - amp ('beam', 'range', 'time')\n", " - amp_b5 ('range_b5', 'time_b5')\n", " - corr ('beam', 'range', 'time')\n", " - corr_b5 ('range_b5', 'time_b5')\n", " - accel ('dirIMU', 'time')\n", " - angrt ('dirIMU', 'time')\n", " - mag ('dirIMU', 'time')\n", " ... and others (see `.variables`)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_dolfyn = ds.velds\n", "ds_dolfyn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## First Steps and QC'ing Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.) Set deployment height" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because this is a Nortek instrument, the deployment software doesn't take into account the deployment height, aka where in the water column the ADCP is. The center of the first depth bin is located at a distance = deployment height + blanking distance + cell size, so the `range` coordinate needs to be corrected so that '0' corresponds to the seafloor. This can be done using the `set_range_offset` function. This same function can be used to account for the depth of a down-facing instrument below the water surface.\n", "\n", "Note, if using a Teledyne RDI ADCP, TRDI's deployment software asks the user to enter the deployment height/depth during configuration. If needed, this can be adjusted after-the-fact using `set_range_offset` as well. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds['vel'][1].plot()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# The ADCP transducers were measured to be 0.6 m from the feet of the lander\n", "api.clean.set_range_offset(ds, 0.6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, the center of bin 1 is located at 1.2 m:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'range' (range: 28)>\n",
                            "array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
                            "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
                            "       13.2, 13.7, 14.2, 14.7])\n",
                            "Coordinates:\n",
                            "  * range    (range) float64 1.2 1.7 2.2 2.7 3.2 ... 12.7 13.2 13.7 14.2 14.7\n",
                            "Attributes:\n",
                            "    units:    m
" ], "text/plain": [ "\n", "array([ 1.2, 1.7, 2.2, 2.7, 3.2, 3.7, 4.2, 4.7, 5.2, 5.7, 6.2, 6.7,\n", " 7.2, 7.7, 8.2, 8.7, 9.2, 9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n", " 13.2, 13.7, 14.2, 14.7])\n", "Coordinates:\n", " * range (range) float64 1.2 1.7 2.2 2.7 3.2 ... 12.7 13.2 13.7 14.2 14.7\n", "Attributes:\n", " units: m" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.range" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.) Remove data beyond surface level" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To reduce the amount of data the code must run through, we can remove all data at and above the water surface. Because the instrument was looking up, we can use the pressure sensor data and the function `find_surface_from_P`. This does require that the pressure sensor was 'zeroed' prior to deployment. If the instrument is looking down or lacks pressure data, use the function `find_surface` to detect the seabed or water surface.\n", "\n", "ADCPs don't measure water salinity, so it will need to be given to the function. The returned dataset contains the an additional variable \"depth\". If `find_surface_from_P` is run after `set_range_offset`, depth is the distance of the water surface away from the seafloor; otherwise it is the distance to the ADCP pressure sensor.\n", "\n", "After calculating depth, data in depth bins at and above the physical water surface can be removed using `nan_beyond_surface`. Note that this function returns a new dataset." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "api.clean.find_surface_from_P(ds, salinity=31)\n", "ds = api.clean.nan_beyond_surface(ds)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds['vel'][1].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.) Correlation filter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once beyond-surface bins have been removed, ADCP data is typically filtered by acoustic signal correlation to clear out spurious velocity datapoints (caused by bubbles, kelp, fish, etc moving through one or multiple beams).\n", "\n", "We can take a quick look at the data to see about where this value should be using xarray's built-in plotting.\n", "In the following line of code, we use xarray's slicing capabilities to show data from beam 1 between a range of 0 to 10 m from the ADCP.\n", "\n", "Not all ADCPs return acoustic signal correlation, which in essence is a quantitative measure of signal quality. ADCPs with older hardware do not provide a correlation measurement, so this step will be skipped with these instruments." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "ds['corr'].sel(beam=1, range=slice(0,10)).plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's a good idea to check the other beams as well. Much of this data is high quality, and to not lose data will low correlation caused by natural variation, we'll use the `correlation_filter` to set velocity values corresponding to correlations below 50% to NaN.\n", "\n", "Note that this threshold is dependent on the deployment environment and instrument, and it isn't uncommon to use a value as low as 30%, or to pass on this function completely." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "ds = api.clean.correlation_filter(ds, thresh=50)" ] }, { "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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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds['vel'][1].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rotate Data Coordinate System" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that the data has been cleaned, the next step is to rotate the velocity data into true East, North, Up coordinates.\n", "\n", "ADCPs use an internal compass or magnetometer to determine magnetic ENU directions. The `set_declination` function takes the user supplied magnetic declination (which can be looked up online for specific coordinates) and adjusts the velocity data accordingly.\n", "\n", "Instruments save vector data in the coordinate system specified in the deployment configuration file. To make the data useful, it must be rotated through coordinate systems (\"beam\"<->\"inst\"<->\"earth\"<->\"principal\"), done through the `rotate2` function. If the \"earth\" (ENU) coordinate system is specified, DOLfYN will automatically rotate the dataset through the necessary coordinate systems to get there. The `inplace` set as true will alter the input dataset \"in place\", a.k.a. it not create a new dataset.\n", "\n", "Because this ADCP data was already in the \"earth\" coordinate system, `rotate2` will return the input dataset. `set_declination` will run correctly no matter the coordinate system." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data is already in the earth coordinate system\n" ] } ], "source": [ "dolfyn.set_declination(ds, 15.8, inplace=True) # 15.8 deg East\n", "dolfyn.rotate2(ds, 'earth', inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To rotate into the principal frame of reference (streamwise, cross-stream, vertical), if desired, we must first calculate the depth-averaged principal flow heading and add it to the dataset attributes. Then the dataset can be rotated using the same `rotate2` function. We use `inplace=False` because we do not want to alter the input dataset here." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "ds.attrs['principal_heading'] = dolfyn.calc_principal_heading(ds['vel'].mean('range'))\n", "ds_streamwise = dolfyn.rotate2(ds, 'principal', inplace=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Average the Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because this deployment was set up in \"burst mode\", the next standard step in this analysis is to average the velocity data into time bins. \n", "\n", "If an instrument was set up to record velocity data in an \"averaging mode\" (a specific profile and/or average interval, e.g. take an average of 5 minutes of data every 30 minutes), this step was completed within the ADCP during deployment and can be skipped.\n", "\n", "To average the data into time bins (aka ensembles), start by initiating the binning tool `VelBinner`. \"n_bin\" is the number of data points in each ensemble, in this case 300 seconds worth of data, and \"fs\" is the sampling frequency, which is 1 Hz for this deployment. Once initiated, average the data into ensembles using the binning tool's `bin_average` function." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [], "source": [ "avg_tool = api.VelBinner(n_bin=ds.fs*300, fs=ds.fs)\n", "ds_avg = avg_tool.bin_average(ds)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
                            "Dimensions:         (range: 28, time: 183, dirIMU: 3, dir: 4, beam: 4, earth: 3, inst: 3, q: 4, time_b5: 183, range_b5: 28)\n",
                            "Coordinates:\n",
                            "  * range           (range) float64 1.2 1.7 2.2 2.7 3.2 ... 13.2 13.7 14.2 14.7\n",
                            "  * time            (time) datetime64[ns] 2020-08-15T00:22:30.001030206 ... 2...\n",
                            "  * dirIMU          (dirIMU) <U1 'E' 'N' 'U'\n",
                            "  * dir             (dir) <U2 'E' 'N' 'U1' 'U2'\n",
                            "  * beam            (beam) int32 1 2 3 4\n",
                            "  * earth           (earth) <U1 'E' 'N' 'U'\n",
                            "  * inst            (inst) <U1 'X' 'Y' 'Z'\n",
                            "  * q               (q) <U1 'w' 'x' 'y' 'z'\n",
                            "  * time_b5         (time_b5) datetime64[ns] 2020-08-15T00:22:29.938494443 .....\n",
                            "  * range_b5        (range_b5) float64 1.2 1.7 2.2 2.7 ... 13.2 13.7 14.2 14.7\n",
                            "Data variables: (12/38)\n",
                            "    c_sound         (time) float32 1.502e+03 1.502e+03 ... 1.499e+03 1.498e+03\n",
                            "    U_std           (range, time) float64 0.04232 0.04293 0.04402 ... nan nan\n",
                            "    temp            (time) float32 14.49 14.59 14.54 14.45 ... 13.62 13.56 13.5\n",
                            "    pressure        (time) float32 9.712 9.699 9.685 9.67 ... 9.58 9.584 9.591\n",
                            "    mag             (dirIMU, time) float64 72.37 72.4 72.38 ... -197.1 -197.1\n",
                            "    accel           (dirIMU, time) float64 -0.3584 -0.361 ... 9.714 9.712\n",
                            "    ...              ...\n",
                            "    boost_running   (time) float64 0.1267 0.1333 0.13 ... 0.2267 0.22 0.22\n",
                            "    heading         (time) float32 3.287 3.261 3.337 3.289 ... 3.331 3.352 3.352\n",
                            "    pitch           (time) float32 -0.05523 -0.07217 ... -0.04288 -0.0429\n",
                            "    roll            (time) float32 -7.414 -7.424 -7.404 ... -6.446 -6.433 -6.436\n",
                            "    water_density   (time) float32 1.023e+03 1.023e+03 ... 1.023e+03 1.023e+03\n",
                            "    depth           (time) float32 10.28 10.26 10.25 10.24 ... 10.14 10.15 10.16\n",
                            "Attributes: (12/40)\n",
                            "    fs:                        1\n",
                            "    n_bin:                     300\n",
                            "    n_fft:                     300\n",
                            "    description:               Binned averages calculated from ensembles of s...\n",
                            "    filehead_config:           {'CLOCKSTR': {'TIME': '"2020-08-13 13:56:21"'}...\n",
                            "    inst_model:                Signature1000\n",
                            "    ...                        ...\n",
                            "    coord_sys:                 earth\n",
                            "    has_imu:                   1\n",
                            "    h_deploy:                  0.6\n",
                            "    declination:               15.8\n",
                            "    declination_in_orientmat:  1\n",
                            "    principal_heading:         11.1897
" ], "text/plain": [ "\n", "Dimensions: (range: 28, time: 183, dirIMU: 3, dir: 4, beam: 4, earth: 3, inst: 3, q: 4, time_b5: 183, range_b5: 28)\n", "Coordinates:\n", " * range (range) float64 1.2 1.7 2.2 2.7 3.2 ... 13.2 13.7 14.2 14.7\n", " * time (time) datetime64[ns] 2020-08-15T00:22:30.001030206 ... 2...\n", " * dirIMU (dirIMU) " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline \n", "from matplotlib import pyplot as plt\n", "import matplotlib.dates as dt\n", "\n", "ax = plt.figure(figsize=(12,8)).add_axes([.14, .14, .8, .74])\n", "# Plot flow speed\n", "t = dolfyn.time.dt642date(ds_avg['time'])\n", "plt.pcolormesh(t, ds_avg['range'], ds_avg['U_mag'], cmap='Blues', shading='nearest')\n", "# Plot the water surface\n", "ax.plot(t, ds_avg['depth'])\n", "\n", "# Set up time on x-axis\n", "ax.set_xlabel('Time')\n", "ax.xaxis.set_major_formatter(dt.DateFormatter('%H:%M'))\n", "\n", "ax.set_ylabel('Altitude [m]')\n", "ax.set_ylim([0, 12])\n", "plt.colorbar(label='Speed [m/s]')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = plt.figure(figsize=(12,8)).add_axes([.14, .14, .8, .74])\n", "# Plot flow direction\n", "plt.pcolormesh(t, ds_avg['range'], ds_avg['U_dir'], cmap='twilight', shading='nearest')\n", "# Plot the water surface\n", "ax.plot(t, ds_avg['depth'])\n", "\n", "# set up time on x-axis\n", "ax.set_xlabel('Time')\n", "ax.xaxis.set_major_formatter(dt.DateFormatter('%H:%M'))\n", "\n", "ax.set_ylabel('Altitude [m]')\n", "ax.set_ylim([0, 12]);\n", "plt.colorbar(label='Horizontal Vel Dir [deg CW from true N]');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saving and Loading DOLfYN datasets\n", "Datasets can be saved and reloaded using the `save` and `load` functions. Xarray is saved natively in netCDF format, hence the \".nc\" extension.\n", "\n", "Note: DOLfYN datasets cannot be saved using xarray's native `ds.to_netcdf`; however, DOLfYN datasets can be opened using `xarray.open_dataset`." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# Uncomment these lines to save and load to your current working directory\n", "#dolfyn.save(ds, 'your_data.nc')\n", "#ds_saved = dolfyn.load('your_data.nc')" ] } ], "metadata": { "interpreter": { "hash": "5cfd453a1a1cce2f32ea80f99ff7da863344217116d39185ac62b248c2577445" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 4 }