From fa25579c2a8c713c73c7f80ee5ceae1ab02ebe09 Mon Sep 17 00:00:00 2001 From: James McVey <53623232+jmcvey3@users.noreply.github.com> Date: Wed, 8 May 2024 12:32:55 -0700 Subject: [PATCH] Various Updates (#127) * Add function * Use better data for test * Ensure that noise subtraction is working properly * Correct error * Update test files with noise-subtracted variables * Should be using along-beam data for adcp turbulence test * Restore one line * Fix doppler noise variable coordinate names * Need to handle 'time' or 'time_b5' * Update changelog * Fix window check * Update documentation * Workarounds for dual profiling instrument * dual profile final fixes * Refactor 'create_dataset' function * Reinstate some skipped ping logic * Add ability to read ID 31, clean up altimeter attrs * Handle variable bottom track beams_cy * Update test attributes * Update changelog * Cleanup * Update notebook * Revert test file * Revert environment change * Attempt to fix things * Don't add extra dims * Add test data * Small fixes * Remove unused code * Update dependency * More clarity on tke in notebooks * Git lfs pointer warning * Fix test data * Add requirements to conda env --- changelog.md | 6 + docs/ADCP_Example.ipynb | 5549 ++++++++++++----- docs/ADV_Example.ipynb | 611 +- docs/apidoc/dolfyn.binners.rst | 6 +- dolfyn/adp/turbulence.py | 74 +- dolfyn/adv/turbulence.py | 35 +- dolfyn/binned.py | 2 +- dolfyn/example_data/dual_profile.ad2cp | 3 + dolfyn/io/api.py | 8 +- dolfyn/io/base.py | 186 +- dolfyn/io/nortek2.py | 224 +- dolfyn/io/nortek2_lib.py | 91 +- dolfyn/tests/data/BenchFile01.nc | 4 +- dolfyn/tests/data/BenchFile01_avg.nc | 4 +- .../data/BenchFile01_rotate_beam2inst.nc | 4 +- .../BenchFile01_rotate_earth2principal.nc | 4 +- .../data/BenchFile01_rotate_inst2earth.nc | 4 +- dolfyn/tests/data/Sig1000_IMU_bin.nc | 3 - dolfyn/tests/data/Sig1000_tidal_bin.nc | 3 + dolfyn/tests/data/dual_profile.nc | 3 + dolfyn/tests/data/vector_data01_bin.nc | 4 +- dolfyn/tests/make_data.py | 1 + dolfyn/tests/test_analysis.py | 57 +- dolfyn/tests/test_read_adp.py | 6 + dolfyn/tools/psd.py | 21 +- dolfyn/velocity.py | 82 +- environment.yml | 8 +- requirements.txt | 2 +- 28 files changed, 4545 insertions(+), 2460 deletions(-) create mode 100644 dolfyn/example_data/dual_profile.ad2cp delete mode 100644 dolfyn/tests/data/Sig1000_IMU_bin.nc create mode 100644 dolfyn/tests/data/Sig1000_tidal_bin.nc create mode 100644 dolfyn/tests/data/dual_profile.nc diff --git a/changelog.md b/changelog.md index 8f2cce09..5616595c 100644 --- a/changelog.md +++ b/changelog.md @@ -13,10 +13,16 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0. - Fix netCDF4 compression encoding - Retain prior netCDF4 variable encoding - Fix bug in reading raw Nortek Signature altimeter data + - Fix bug where noise input wasn't being subtracted from auto-spectra + - Fix bug that would error out when entering custom FFT window - API/Useability - Updates to support python 3.10 and 3.11 - Added ability to read Nortek AWAC waves data + - Added ability to subtract Doppler noise in TKE dissipation rate functions + - Added function to calculate turbulence intensity and remove noise + - Add ability to read Nortek dual profiling instruments + - Add ability to read ID 31 (initial altimeter scan for averaged altimeter measurements) - Nortek Vectrino (.vno) - Add support for Nortek Vectrino (.vno) files. diff --git a/docs/ADCP_Example.ipynb b/docs/ADCP_Example.ipynb index 8728c02f..55013099 100644 --- a/docs/ADCP_Example.ipynb +++ b/docs/ADCP_Example.ipynb @@ -1,1584 +1,3969 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ADCP Example" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following example shows a typical workflow for analyzing ADCP data using DOLfYN's tools.\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 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 DOLfYN tools through MHKiT:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Import core DOLfYN functions\n", - "import dolfyn\n", - "# Import ADCP-specific API tools\n", - "from 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 Signature 1000, 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://dolfyn.readthedocs.io/en/latest/about.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." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading file ../dolfyn/example_data/Sig1000_tidal.ad2cp ...\n" - ] - } - ], - "source": [ - "ds = dolfyn.read('../dolfyn/example_data/Sig1000_tidal.ad2cp')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are two ways to see what's in a DOLfYN 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": [ - "
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-                                                 "    telemetry_data       (time) uint8 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0\n",
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-                                                 "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",
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" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 55000, dirIMU: 3, dir: 4, range: 28, beam: 4,\n", - " earth: 3, inst: 3, q: 4, time_b5: 55000,\n", - " range_b5: 28, x: 4, x*: 4)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2020-08-15T00:20:00.500999927 ...\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 in DOLfYN 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": [], - "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": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "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": 6, - "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": 7, - "metadata": {}, - "outputs": [], - "source": [ - "api.clean.find_surface_from_P(ds, salinity=31)\n", - "ds = api.clean.nan_beyond_surface(ds)" - ] - }, - { - "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": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "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": 9, - "metadata": {}, - "outputs": [], - "source": [ - "ds = api.clean.correlation_filter(ds, thresh=50)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Review the Data" - ] - }, - { - "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": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data is already in the earth coordinate system\n" - ] - } - ], - "source": [ - "dolfyn.set_declination(ds, declin=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." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "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": [ - "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 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 `do_avg` function." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "avg_tool = api.VelBinner(n_bin=ds.fs*300, fs=ds.fs)\n", - "ds_avg = avg_tool.do_avg(ds)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Two more variables not automatically provided that may be of interest are the horizontal velocity magnitude (speed) and its direction, respectively `U_mag` and `U_dir`. There are included as \"shortcut\" functions, and are accessed through the keyword `velds`, as shown in the code block below. The full list of \"shorcuts\" are listed [here](https://dolfyn.readthedocs.io/en/latest/apidoc/dolfyn.shortcuts.html).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "ds_avg['U_mag'] = ds_avg.velds.U_mag\n", - "ds_avg['U_dir'] = ds_avg.velds.U_dir" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting can be accomplished through the user's preferred package. Matplotlib is shown here for simplicity, and flow speed and direction are plotted below with a blue line delineating the water surface level." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "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='Horizontal Vel [m/s]')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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qrsG3Ftswm6EOcVFKOxL0to+LVFK7SCW1i1ByuwilxkYpOyVGHZNiGKtHjX98COQgXQEdECyEESCHkMhwm3plJahXVoKuPSNXknSgqlardpVpze4yfbe7XN8VlKm8pl6rdpVp1U+eaU6MidDA3CSdlpus03KTNSA3iVdOAQAAoEnlNfXaur9KJVV1Kq2u08FDdSqpqm18/em+ShUcrPEpn5TYSKUnRCszMVrpCdFKT4hSRkK0MxhOS4hSamyUwmjQ8R1drIMiqBHUwoUL9Ze//EX5+fkqLCzUnDlzNH78eOd60zT1yCOP6KWXXlJpaamGDBmiv/3tb+rdu3fwKh1g7eOidEGvdF3QK11S4zHZWXJI3+0u03cF5fpud5nW7SlXeU295m/cr/kb90uSwm2G+uckaeiRVurTcpMVHREWzF0BAABAkBxtCd60r1Kbiiq1qbhKm4oqVVRx2Ou27eOi1Cm1nTISo5WREK2MI0Hw0fm0hChFhXOf2eJCeBTrF198US+++KJ27NghSerdu7f+8Ic/aMyYMZJ8i+Nqa2s1ZcoUzZ49WzU1NRo1apReeOEFZWdnt+hu+SuoAXJ1dbX69++vG2+8UVdccYXb+ieffFJPPfWUZs6cqR49eujRRx/VBRdcoI0bNyo+Pj4INQ4+wzDUqX2sOrWP1aUDGt/XXG93aENhhVbuLNXKXWXK31mqPWU1zvc0P/fVFkWG2zQ4L/lIwNxe/bITFRHGc8wAAAAnm6raBq3dXa7VBWVaXVCq7wsrmmwJzkiIVnpitFLaRSg5NlLJ7SKVl9pO3dPi1SM9TqlxUQGsPY4K5S7W2dnZeuKJJ9StWzdJ0muvvaZLL71Uq1atUu/evX2K4+655x7961//0ttvv63U1FTde++9Gjt2rPLz8xUWFrw/uBhmiHQ2NwzDpQXZNE1lZWXpnnvu0e9//3tJjX9lSE9P15///GfdeuutHvOpra1VbW2tc76iokI5OTkqLy9XQkLCCd+PUFFw8FDjYF9bS/TtlgMqrqx1WR8XFa7hPTpo1KlpGtkzTcmxJ8n74gAAANqIw/V2rd9brs37qrT9QLW27q/WtgNV2nGgWg4Pd/jt46LUMyNO3dPi1TOj8ZWl3dPjlHCSPe9bUVGhxMTEVnv/f7T+c/94q2KjA3ePXn24Thf84f+poKDA5bhFRUUpKsr7H0lSUlL0l7/8RTfddJPXOK68vFwdOnTQG2+8oauvvlqStHfvXuXk5OjTTz/VhRdeeGJ20gch+5Dq9u3bVVRUpNGjRzuXRUVFafjw4Vq0aNExA+Rp06bpkUceCVQ1Q1ZOSjvlpLTThME5Mk1T2w5Ua9HWEi3eekCLt5ao9FC9PllbqE/WFspmSIPzUjSsR3ud072D+nZM5PkQAACAEOJwmNp18JDW763Qyl2NvQTX7y1Xvd1zW1dWYrQG5CZpQE6S+nZsfAtKCg0irUuQnkHOyclxWfzwww9r6tSpx9zMbrfrn//8p6qrq3XWWWf5FMfl5+ervr7eJU1WVpb69OmjRYsWESB7UlRUJElKT093WZ6enq6dO3cec7v7779fkydPds4fbUFuywzDUNcOceraIU43nJknh8PU2j3l+nLDPn25oVgbCiu0bMdBLdtxUP/3xSYlRIdraNf2Ou/UNJ1/ajoXUwAAgAArLK/R4q0lyt9Zqg2FFdpYVKnqOrtbuvZxUeqdlaDO7WPVpUOsurSPU4/0OKUlRAeh1jgZeGpB9mTt2rU666yzdPjwYcXFxWnOnDnq1auXFi1aJKnpOK6oqEiRkZFKTk52S3M0DgyWkA2Qjzr6PrOjTNN0W/ZTvnYBaMtsRwbw6p+TpHtH99Tu0kOat3G/vtm8X4u2lqjicIM+W1+kz9YXKcxm6IxOKbqoT4ZG905XZmJMsKsPAABw0imuPKwl2w5q8ZEefztKDrmliQy3qWd6vAbkJGlQXrIG5SUrOzmmyXtjtGIOUx77yp/I8iQlJCT41DW9Z8+eWr16tcrKyvT+++9r4sSJWrBggXO9v3Gcr2lOtJANkDMyMiQ1/nUhMzPTuby4uNjtrxE4PtnJ7XTDmXm64cw8NdgdWrOnXAs37dcX6/fp+8KKxmeZt5Xo4Y/Xq39Oki7sna4Le2eoa4e4YFcdAACgVTpcb9fCTfv19eYDWrytRFuKq1zW2wypb8dEDemSqj4dE3VqRrw6t49VOIOsthmhPEiXJEVGRjoH6Ro8eLCWL1+uZ555xvnccVNxXEZGhurq6lRaWurSilxcXKyhQ4ce764cl5ANkDt37qyMjAzNnTtXAwcOlCTV1dVpwYIF+vOf/xzk2p28wsNszncp33N+D+0qOaQvvi/S5+uLtGJnqb4rKNN3BWV68rONOiUjXhMG5+jy0zoqqR3dsAEAAJpS22DX15sO6JO1hZr7/T5V1TY41xmGdGpGgvMVnad3TjnpBs+Cn1rZe5BN01Rtba1PcdygQYMUERGhuXPnasKECZKkwsJCrVu3Tk8++eTx7cdxCmqAXFVVpS1btjjnt2/frtWrVyslJUW5ubm655579Pjjj6t79+7q3r27Hn/8cbVr107XXXddEGvdtuSmttMvz+2iX57bRcWVh/Xl98X6fH2RFm09oB+KKvXHf3+vJz77QWP6ZOjq03M0pHMqA3wBAABIarA7tG5vhRZvLdGSbSVaseOgy3PEmYnRGt0rXWd1ba8zu6TQ4ABXIRwgP/DAAxozZoxycnJUWVmpt99+W/Pnz9dnn30mwzC8xnGJiYm6+eabde+99yo1NVUpKSmaMmWK+vbtq/PPP/9E7aFPghogr1ixQiNHjnTOHx1ca+LEiZo5c6Z+97vfqaamRrfffrvzBdNffPFFm30HcrClxUfruiG5um5Irspr6vXx6j2avaxA3xdW6KPVe/XR6r1qHxepC3plaEyfDJ3VNZV3LQMAgDaltLpO8zcV68vvi7Vg036XVmKp8Z3DF/fN1CX9MjUwJ0k2GhZwDKbDlBnAZ5D9KWvfvn264YYbVFhYqMTERPXr10+fffaZLrjgAknyKY6bPn26wsPDNWHCBNXU1GjUqFGaOXNmUN+BLIXQe5BPlNb+HrRQZ5qNI2LPXlagT9cWqrym3rkuMSZC4/pn6erTc9SnY2IQawkAAHDiHKiq1adrC/XJmkKt2Fkq+08CjcSYCA3pnKIzu6TqzC6pOiUjnqD4BGvt9/9H6//5/ZMC/h7kC6fNbLXHraWE7DPIaB0Mw1C/7CT1y07SHy/trcVbS/TZ+iJ9sb5IB6rq9MaSnXpjyU716Zigq0/P1di+mUrmtVEAAKCVqzxcr7nf79NHq/fqmy0HXILiUzLidUGvdI06NV39OiYSEAOtCAEyWkxEmE3DenTQsB4d9KdL+2jR1gN6Z3mBvli/T+v2VGjdnnWa+vF6ndklRRf1ztDo3hlK5x19AACglSitrtPcDfv0+boifb35gOrsDue6/tmJ+ln/LF3YO0M5Ke2CWEucNEL4GeSTGQEyTogwm6Fzu3fQud076GB1neas2qP38ndrQ2GFvt1Som+3lOihj9br9E7JuuK0bF3cL5ORGgEAQMgpr6nX5+uL9K/v9mrR1hKXluIu7WM1bkCWxvXPUhdef4kWZirAr3kSAbJEgIwASImN1M3ndNbN53TWzpJqfb6+SJ+v36f8naVavqNxevjj9RrdO0OXn9ZR53Rrz+BeAAAgaA7X2/XfDcX6aPUezd+436Wl+NTMBF3UO0MX9clQj/Q4GQbdp3GCOMzGKZDlgQAZgZWXGqtfDeuqXw3rqqLyw/pwdWPL8pbiKv3ru73613d7ldwuQhf1ydDYflk6swuvjQIAACdevd2hb7Yc0L9W79Xn64tcXsfUPS1Olw7I0th+WerUPjaItURbQg/r4CBARtBkJEbrtuFddeuwLlqzu1zvr9ytT9cW6kBVnWYvK9DsZQXqEB+lKwdl65rTc5SXyg8SAABoOQ12hxZvK9Gnawv1+fp9Olhd51zXMSnG2X36lIx4WooReETIQUGAjKAzDEP9c5LUPydJfxjbS0u3H9S/vturz9YXaX9lrV6cv1Uvzt+qs7ul6prTc3VBr3RFRwT3/WgAAKB1Mk1TS7cf1Ier9ujz9UUqPfTjKypTYyM1tl+mxg3I0mm5yQTFCC4C5KAgQEZICQ+z6exu7XV2t/b646V99NUP+/TWsgJ9vXm/c3Cv+OhwjemTofEDOmoIXbABAIAPisoP6/2Vu/XuigLtLDnkXJ4SG6kLe2fo4r4ZOqtLqsIZBwUhwjQDPEgXAbIkAmSEsMhwmy7qk6mL+mSq4OAhvbuiQO/l71Zh+WG9u2K33l2xW+kJUbr8tGxdPTiHZ4IAAICLugaH/rthn95dUaAFm/Y7xyCKjQzT2H5ZGjcgS0M6pxAUA3AiQEarkJPSTveO7qn/Ob+Hlu04qI9W79Enawq1r+LHLthndknRNafn6qI+GXTBBgCgjTJNU98XVuiDlXs0Z9Uel+eKz+iUoqsGZ+vivpmKjeI2GCGOUayDgisDWhWbzdCZXVJ1ZpdUTR3XW19tKNY7R/4qvGTbQS3ZdlCJH0fosoEddfXpOTo1MyHYVQYAACeY3WEqf2epPl9fpC++L1LBwRrnurT4KF0xKFtXDcrmXcVoVXgEOTgIkNFqRYWHaUzfTI3pm6k9ZTV6b0Xjc0V7ymo0c9EOzVy0Q/2zEzXh9Bz9rH+WEqIjgl1lAADQgnaXHtLsZbv07ord2l9Z61weFW7TyJ5pumpwtob36EAXarRORMhBQYCMk0LHpBj95vzuuvO8bvp2ywG9s7xAX3xfpO92l+u73eX647++10V9MnTVoBwN7ZoqGwN7AQDQKtkdphZu2q9ZS3Zq3sZiZ6/QxJgIjTolTaN7Z2hYj/ZqF8ltLlo5AuSg4MqBk0qYzdCwHh00rEcHlVTV6oOVe/TP/AJt2lelj1bv1Uer9yo7OUaThnbSNWfkKo7njwAAaBW27q/Se/m79cHK3dpX8WNr8dCuqfr5mXm6oFe6ImgpxknEdDROgSwPBMg4iaXGRemWYV30y3M7a83ucr27okAff7dXu0tr9OgnG/TMfzfr+iF5uvHsTkpPiA52dQEAgEXF4Xp9sqZQ/1xRoJW7ypzLk9pF6PKB2br+zFx15blinKxoQQ4KAmSc9AzDUP+cJPXPSdJDY3tpzqo9+sfCbdp2oFp/X7BVr3yzTRf2ztB1Z+TqrK6pMgy6XwMAECwOh6lFW0v0Xn6BPltfpMP1jc1aNkMa0TNNVw7K1qhT0xQVzhsrALQ8AmS0KdERYbr2jFxdPThH//2hWP9YuE3LdhzUv9cU6t9rCtUptZ2uPSNXVwzKVvu4qGBXFwCANqOqtkHvrSjQjEU7tLPkkHN5t7Q4XTUoW5cN7Kg0enyhDWlsQA5cqy4NyI0IkNEm2WyGLuiVrgt6pWvdnnLNXrZLH63eqx0lhzTtPz/o/77YSKsyAAABsKvkkN5YskNvLy9Q5eEGSVJ8dLguHZClKwflqH92Ir/DaJvMI1MgywMBMtCnY6Ieu6yvHrj4VP17zV69tXSXvttdTqsyAAAnSMHBQ/p0baE+WVuoNbvLncu7tI/Vjed01hWndWQUarR5pmkGuAWZCFkiQAacYqPCdfXpubr69NxjtiqP7p2h68/I1ZldeFUUAAD+qGtw6D/rCvXaoh0uA27ZDOnsbu1109mdNbxHB35fgaNMSYEcWZr4WBIBMuDRsVqVP1lTqE/WFKpLh1j96twuuuy0jgwSAgBAEw5W1+mtpTv1xpKdztczGYZ0ZudUXdwvUxf1zlCHeHpoAVa0IAcHATLQhGO1Km/bX637Plirp+Zu0s3ndNZ1Q3IVHx0R7OoCABAyNhZVasa32zVn1R7VNjQ2g3WIj9INZ+bpmtNzGHAL8IbXPAUFATLgo6OtyvdffKpmL92lV77ZrqKKw5r2nx/03FdbdHHfDF1xWrZO75RC9zAAQJvkcJiat7FYr367Xd9uKXEu79sxUTed00mX9M1SZLgtiDUEgKYRIAN+iosK1y3Dumji0E76cPUe/b8FW7V1f7XeXbFb767YrezkGF0+sKOuOSNXWUkxwa4uAAAn3NFXNM1ctEM7jryiyWZIF/XJ0E1nd9agvGRGogb8RANycBAgA80UGW7ThME5uvK0bC3fcVAfrNyjT9YWandpjZ79aov+Nn+rLuydrklDO+v0TtwYAABOPjsOVGvWkp16Z3mBKmsbX9GUEB2ua8/I1Q1n5Sk7uV2Qawi0YkTIQUGADBwnm83QkC6pGtIlVY9c2ltffL9Ps5fu0uJtJfp0bZE+XVukXpkJmjS0k8YNyFJ0BIN6AQBar4rD9fp0TaHey9+tFTtLncu7dIjVjWfziiagpZiOximQ5YEAGWhR0RFhGtc/S+P6Z+mHogq9tmin5qzare8LK/S799do2n826NozcvXzM/Pofg0AaFXW7SnXq99u16drC3W4vvFO2mZIw3t00C+GdtLw7ryiCWhRpgL76iUakCURIAMnzCkZCZp2eV/9/qKeemd5gV5fvFN7ymr0wvyt+n8Lt+n8U9N03ZA8ndutPTcUAICQZHeYmvv9Pr367XYt237Qubx7WpyuHJSt8QM7Kp3RqIETgtc8BQcBMnCCJbWL1K3Du+qX53bRlxv2aea3O7R4W4k+X79Pn6/fp45JMbrm9BxNOD2HmwwAQEioOFyvd5c3Drq1u7RGkhRuMzS2X6YmDu2kATlJjK0BnGi0IAcFATIQIGE2Qxf2ztCFvTO0aV+lZi/bpffzd2tPWY3+OneTnvnvZo3tl6mbzumsftlJwa4uAKAN2llSrZmLduifK3ar6sigW0ntInT9kFzdcGYnZSTyh1wAJzcCZCAIeqTH6+Gf9dbvLzpF/1lXqDeX7NKKnaX6cPVefbh6rwbnJevmczrrgl7pCg/jfZEAgBOnrsGhLzfs0+xlu/TNlgPOgWy7p8XppnM6a/yAjoqJZIBJINDoYh0cBMhAEEVHhOmygdm6bGC21u5uHPzk32v2asXOUq3YWaqOSTG68exOmnB6jhKiI4JdXQDASWRnSbXeXNrYm6mkus65fHiPDrr5nM46t3t7ulEDwUQX66AgQAZCRN/sRE2/eoDuG3OKZi3ZqVlLGgf1evSTDZo+d5OuGpyjG8/upLzU2GBXFQDQSpmmqSXbDurVb7fryw37nK3FafFRumpwtq4enKvcVN5dDIQCXvMUHATIQIhJT4jWvaN76o6R3TRn1R69+s12bS6u0sxFO/Ta4h06/9R03XR2Z53ZJYW/7AMAfFJvd+hf3+3VP77erg2FFc7lw3p00A1n5mlkzw480gOEmgB3sRZdrCURIAMhKzoiTNeekatrTs/R15sP6NVvt2v+xv2a+/0+zf1+n3plJuimczrrZ/0zFRXOs2EAAHc1dXa9s3yX/vH1du0paxyNOiYiTFcM6qhJQzurW1pckGsI4JjoYh0UBMhAiDMMQ8N6dNCwHh20pbhSM77dofdX7tb3hRWa8s/v9MR/ftANZ+bp+jNz1T4uKtjVBQCEgKLyw5q9bJfeWLJTB488X9w+LlI3nt1Z1w/JVVK7yCDXEIA3phnYRl0akBsRIAOtSLe0eD12WV9NGd1Ts5fv0uuLdqqo4rCmf7lJf5u/RVcOytYdI7upY1JMsKsKAAgw0zS1aGuJ3li8U3M37JPd0Xi3m5MSo1uHddWVg7IVHUGPI6D1CHCETBOyJAJkoFVKjo3U7SO66ZZzu+jTtYV69Zvt+m53ud5aukv/XFGgCYNzdDuBMgC0CfV2hz5ctUd/X7BVW/dXO5ef3ilZN5zVSRf3yeD5YgDwEQEy0IpFhNl06YCOGtc/S8u2H9SzX23Wt1tK9ObSXXp3RYGuHJStG8/urB7p8cGuKgCghR2ut+ud5QV6aeE25/PFcVHhumxgR11/Zq5OyUgIcg0BHA+6WAcHATJwEjAMQ0O6pOrNLqlauq1Ez/x3sxZtLdHsZQWavaxAZ3dL1Y1DO2vkKWkKszHyNQC0ZiVVtZq1ZJfeWLJDB6qOPl8cpVvO7azrhuQqPjoiyDUE0BIIkIODABk4yQzpkqq3uqRqxY6DeuWb7fp8fZG+3VKib7eUqFNqO909qrsuHdCRQBkAWpktxZV65Zvt+mDlHtU2NL6wNDs5RrcO76qreL4YOPk4zMYpkOWBABk4WQ3ulKLBnVK0u/SQ3liyU28vK9COkkOa/O53emH+Vk2+oIcu6p0hG4EyAISsowNvvfz1Ns3buN+5vF92on55bheN6ZOhCJ4vBk5KtCAHBwEycJLLTm6n+8ecqt+M6q7XFu3U3xds1ZbiKt3+5kr1zkrQzed01sV9M2l5AIAQUttg17++K9TLX2/TD0WVkiTDkEb3Stcvz+2iwXnJMgz+wAmczAiQg4MAGWgj2kWG69cjuur6M3P18tfb9crX27R+b4Umv/udHv1kgyYMztH1Q3KVk9Iu2FUFgDZrZ0m13lq2S/9csdv5/uJ2kWGaMDhHN57dSXmpsUGuIYCAMRXYNy8RIEtqQwHyri27FR/XOJKv6cOn7y2F9W+21vQOOTxsY3iZbzq9J3k9crymAX4qITpCky/ooUlDO+mtpTv11tJd2lt+WH9fsFX/b+FWXdw3U3ef1109Mxj5GgACwe4w9dUPxXp98Q59vfmAc3lmYrQmDu2ka0/PVWI7Bt5CaLgpc6zLfIPpes9b67C7bdNguVO2prFb1lvzDPPQW6LOdM2jUvU/5mc2uKUHfNVmAmQArlJiI3Xned112/Cu+uqHYr2xZKe+3nxAn6wp1KdrCwmUAeAEq6pt0D9XFGjmoh3aWXJIUmM36mHdO+j6Ibk675Q03l8MtGGmacoMYL/nQJYVygiQgTYuPMym0b0zNLp3hn4oqtCz/92sT9cW6ZM1hfpkTaHOPzVdvzgrT+d0a8+AXgDQAgoOHtJri3boneUFqqxtbOlKjInQNWfk6Poz8pSbyqMuAHgGOVgIkAE4nZKRoBeuH+QSKH+5YZ++3LBPndvH6vohubpqUA5d/QDAT6ZpKn9nqfP1e0ffptKlQ6xuOruzLj+to9pFclsG4Cd4BjkouBIDcHM0UN5SXKlZS3bp/fzd2n6gWo9+skFPzd2kq0/P0c3ndFZ2Mq0cANCUugaH/rOuUK98s11rdpc7l5/bvb1uOruzhvfoQO8cAB7RghwcBMgAjqlbWrymjuut317YUx+t3qvXF+/QD0WVmvHtDr2+eKcu6ZupXw3roj4dE4NdVQAIKaXVdXpr2S69vniH9lXUSpIiw226fGBH3Xh2Z8Z3AOCV6WicAlkeCJAB+CA2KlzXDcnVtWfk6OvNB/TSwm36ZssBffzdXn383V4NzE3S9UPyNLYf71MG0HaZpqlVBWWavXSX/rVmrw7XN95tdoiP0i/OzNN1Q3KVGhcV5FoCaC1oQQ4OAmQAPjMMQ8N6dNCwHh20bk+5Xlq4TZ+uLdSqXWVatatMf/r397ritGxdf2auunaIC3Z1ASAgymvq9eGqPZq9bJd+KKp0Lu+dlaCbz+msS/plKiqcPx4CQGtAgAygWfp0TNSz1w7Ug2NP1T9X7NZbS3dpT1mNXv12u179drvO6pKq68/M1eheGYoM5zUlAE4upmlq5a5SvbW0QP9es1e1DY2txVHhNo3tl6Vrz8jRoLxkGR7e3woAPqEJOSgIkAEcl7T4aN0xsptuG95VCzft15tLd+qrH4q1eFuJFm8rUfu4SE0YnKNrz8hVTgqDegFo3coO1WnOkdbiTfuqnMtPyYjXtWfkavyAjoz0D6BFEB8HBwEygBYRZjM08pQ0jTwlTXvKavTOsl16e3mBiitr9cL8rXpxwVaN6NFB1w/J08hT0hTGqK0AWgnTNLViZ6lmL92lT9YWOluLoyNs+lm/LF07JFcDc5JoLQbQogiQg4MAGUCL65gUo8mje+quUd313w379ObSXfp68wHN27hf8zbuV1ZitK45I1dXn56j9IToYFcXADwqra7TB0dai7cUu7YWXz8kV5cO7KiEaFqLAZwYBMienXbaaX6lNwxDH3/8sTp27OhTegJkACdMRJhNF/XJ1EV9MrX9QLVmL9ulf64o0N7yw3pq7iY989/NGt0rXdcPydPQrqm8CxRA0DkcppbtOKi3l+3Sp+uKVHektTgmIkzj+je2FvfPTqS1GEBgtJKgNZBWr16te++9V3Fx3geENU1TTzzxhGpra33OnwAZQEB0bh+rBy4+VZMv6KHP1hXpzaU7tXxHqf6zrkj/WVekTqntdN2QXF05KEcpsZHBri6ANmbTvkp9uGqPPlq9V3vKapzLe2Um6Lohubp0QJbiaS0GEEC0IB/bb3/7W6WlpfmU9q9//atfeRMgAwio6IgwjR/YUeMHdtTGokq9tXSnPli5RztKDunxT3/Q/32+SRf3zdD1Z+ZpMCPAAjiBKg43vp7p7WUF+r6wwrk8LipcY/tl6rohuerbkdZiAAgl27dvV4cOHXxO//333ysrK8vn9ATIAIKmZ0a8Hrm0j3530Sn613d7NWvpTq3bU6EPV+/Vh6v3qmd6vK4/M1fjec4PQAtat6dcby7dqY9W79WhOrskKSLM0PAeabpsYEeNOjVN0RG8txhAcNGC7FleXp5f6XNycvxKT4AMIOhio8J1zRm5uuaMXK3ZXaY3l+zSR9/t0cZ9lfrDR+v1xH9+0PVDcnXLsC5Ki2dQLwD+q6mz699r9mrW0l36rqDMubx7WlzjgFsDOiqZxzsAhBAC5GPbtWuXT+lyc3P9zpsAGUBI6ZedpH5XJumBS07Vh6v2aNaSndpcXKV/fL1dry3eqWtPz9Gtw7sqKykm2FUF0Aps3lep2csK9F5+gSoON0hqbC0e0ydT1w/J1RmdU+hCDSAkESAfW6dOnTxeu03TdC43DEMNDQ1+502ADCAkJcZEaOLQTvrFWXmav2m/nvvvZq3cVabXFu/UW8t26ZK+mbrhrE46LZd3jwJwVVpdp4+/26v3V+7Wmt3lzuU5KTG67ow8XTU4W+3jooJYQwDwganAjmLdigLkVatWeVxumqbefvttPfvssz6Ncu0JATKAkGYYhkb2TNOIHh20eGuJnvtqixZvK3E+p9wrM0E3nJWnSwdkqV0klzSgraprcGjexmK9n79b8zYWq97eeKcXbjM08pQ0XTckV8O7d+B1cgBaDVqQj61///5uy7788kvdd9992rRpk373u99pypQpzcqbu0kArYJhGBrarb2GdmuvNbvLNGtJ4wA73xdW6P4P1urxTzboikHZ+vmZeeqW1ry/GAJoXUzT1No95Xo/f7c+/m6vSg/VO9f16ZigK07L1s/6Z9FaDKB1CnCA3JpakH8qPz9f9913n77++mv98pe/1KeffurzK6A8IUAG0Or0y07Sk1cm6YGLT9V7+bs1a8lO7Sg5pJmLdmjmoh0a2jVVN5yZp/N7pSsizBbs6gJoYaXVdZqzao/eXVGgH4oqncs7xEfpsoEddcVp2eqZER/EGgIATrQtW7bof//3f/X+++9rwoQJ+v7779WlS5fjzpcAGUCrldQuUr88t4tuOruzvtlyQG8s2an/btinRVtLtGhridITonTtGbm69oxcpScw+jXQmjkcpr7ZckDvrCjQ3PX7VGd3SJIiw226sHeGrjito87p1l7h/FEMwEmCLtbHdvvtt+uVV17RyJEjtWLFCg0YMKDF8iZABtDq2WyGhvXooGE9OmhPWY1mL92lt5fv0r6KWj395WY9/9UWXdg7Q788t7MG5iYHu7oA/LC79JD+uWK33svfrT1lNc7lvbMSdPXpObq0f0cltuM96QBOPgTIx/b3v/9d0dHRKi4u1k033XTMdCtXrvQ7bwJkACeVjkkxmnJhT909qrs+W1+kWYt3atmOg/pkbaE+WVuoMzql6JZhXTTqlDQG6wFCVMHBQ/p8fZE+X1+kFTtLnTdtCdHhumxgR101OEd9OiYGt5IAcIIRIB/bww8/fMLybjMBsnFk+nFOx5hrZD0/TMsSm2Ur9/PJvYuXdRtvfEm9a1NBk/WwtcDT9nbLfIPpcJl3eDk2noQZTXeB8yUP9xws9XLUuc43uM570rlPb69p0DpEhts0rn+WxvXP0obCCr36zXZ9uHqPlu04qGU7Dqpz+1iNH9BRl/TLZFAvIAQUHDykj7/bq0/WFOr7wgqXdWd3S9WEwTm6sHeGoiPCglRDBMOY9ue5LXNY7uJtllf9Wddb3wRo9xAFhHu5L7HeBxpu94He77e8bWO99/F+rylFWOrtrZ7hloNh3V7ydPyarleYdb2HVy/avByeMOu9uZfPo3Eb1zQ/LaM1BXpN4jVPx0SADADH4dTMBP3lqv6acmFPzVy0Q7OW7NT2A9Wa/uUmTf9yk07JiNclfTN1Sb9MdelAsAwEyv7KWn26tlAfrd6jlbvKnMtthnRG5xRd1DtDo3tnKCspJniVBIAgoQU5OAiQAbQZ6QnR+v1Fp+iOkd30+boifbK2UF9v3q8fiir1Q1Gl/jp3k07NTNDYfpm6uG+mOrePDXaVgZOK3WFqze4yzd+4X/M3Fuu73eXOdTZDGtq1vX7WP1MX9MpQSmxkEGsKAMEXygHytGnT9MEHH+iHH35QTEyMhg4dqj//+c/q2bOnM82kSZP02muvuWw3ZMgQLVmyxDlfW1urKVOmaPbs2aqpqdGoUaP0wgsvKDs7+7j3p7kIkAG0OXFR4bpiULauGJSt8kP1+uL7xmD5m80HtKGwQhsKK/SXzzeqd1aCLumXqUv6ZiovlWAZaI6yQ3VasGm/5m/crwWb9utgtevjLv1zknRp/yyN7ZepNEabBwCnUA6QFyxYoDvuuEOnn366Ghoa9L//+78aPXq0vv/+e8XG/njPdNFFF2nGjBnO+chI1z9+3nPPPfrXv/6lt99+W6mpqbr33ns1duxY5efnKywsOI/UECADaNMS20XoqsE5umpwjsoO1emL9fv077WF+nbLAa3fW6H1eyv05Gcb1bdjojNYzklpF+xqAyHLNE2t31uh+RuLNW/jfq3aVSrHT2664qPCNaxHBw3v2UEjenQgKAaAVuizzz5zmZ8xY4bS0tKUn5+vYcOGOZdHRUUpIyPDYx7l5eV65ZVX9MYbb+j888+XJM2aNUs5OTn68ssvdeGFF564HWgCATIAHJHULlITTs/RhNNzVFpdp8/XN7YsL9paorV7yrV2T7me+M8P6pedqEv6NnbDJlgGpIrD9fp28wHN21is+Rv3q7iy1mX9KRnxGtEzTSN7dtBpecmK4F3FAOBVsFqQKypcB0qMiopSVFRUk9uWlzc+MpOSkuKyfP78+UpLS1NSUpKGDx+uxx57TGlpaZKk/Px81dfXa/To0c70WVlZ6tOnjxYtWuR3gHz48GFFRx//H10JkAHAg+TYSF1zRq6uOSNXJVW1+nz9Pn26tlCLth7Qmt3lWrO7XNP+84N6pMfpvFPSNerUNA3MSVI4N/5oA0zT1KZ9VUdaiYu1YkepGn7STNwuMkxnd2uvkT3TNKJnBwbZAoBmCFaAnJOT47L84Ycf1tSpU5vYztTkyZN1zjnnqE+fPs7lY8aM0VVXXaW8vDxt375dDz30kM477zzl5+crKipKRUVFioyMVHJyskt+6enpKioq8qnODodDjz32mP7+979r37592rRpk7p06aKHHnpInTp10s033+zbzv8EATIAeJEaF6XrhuTquiG5OlBVq8/XF+nf3xVq2Y6D2rSvSpv2VenvC7YqqV2ELuyVobH9M3VWl1SCZZxUqmsbtGhrSWMr8Q/F2lt+2GV91w6xR1qJ03R652RFhfM6JgA4HsEKkAsKCpSQkOBc7q31+M4779SaNWv0zTffuCy/+uqrnf/u06ePBg8erLy8PH3yySe6/PLLm6iH6fZ6sWN59NFH9dprr+nJJ5/ULbfc4lzet29fTZ8+nQAZAE609nFRun5Inq4fkqfyQ/VasHm/vtqwT/M37VfZoXq9s6JA76woUEpspC7qk6ELTk3XWV1TeXcrWh3TNLX9QLXmHRlxeum2g6qz//i++ahwm4Z2TdXIU9I0okeaclN53AAAWpKpAAfIR/4/ISHBJUBuyl133aWPP/5YCxcu9DrydGZmpvLy8rR582ZJUkZGhurq6lRaWurSilxcXKyhQ4f6VP7rr7+ul156SaNGjdJtt93mXN6vXz/98MMPPuVhRYAMAM2U2C5C4/pnaVz/LDXYHVq246D+vaZQn60r0sHqOr21dJfeWrpL0RE2De3aXuedkqaRp6SpI91NEaKKKw9r8dYSLd5aokVbS7Tr4CGX9bkp7TSyZweNOCVNZ3XhDz8AcCKF8ijWpmnqrrvu0pw5czR//nx17tzZ6zYlJSUqKChQZmamJGnQoEGKiIjQ3LlzNWHCBElSYWGh1q1bpyeffNKneuzZs0fdunVzW+5wOFRfX+/7Dv0EATIAtIDwsMYgeGjX9vrjuN5avK1E/1lXpHk/FKuw/LC++qFYX/1QLKlxwKKRp6TpvFN4bhnBZZqm1uwu12frizT3+33aUlzlsj4yzKYhXVI04sizxF3ax/rc7Q0AcHxCOUC+44479NZbb+mjjz5SfHy885nhxMRExcTEqKqqSlOnTtUVV1yhzMxM7dixQw888IDat2+vyy67zJn25ptv1r333qvU1FSlpKRoypQp6tu3r3NUa2969+6tr7/+Wnl5eS7L//nPf2rgwIG+79BPECADQAsLD7Pp3O4ddG73DjJNUz8UVeqrH4o174dirdxVqh+KKvVDUaVenL9ViTERGt6jg847JU3DenRQSmyk9wKA41BaXaeVu0r1zZYD+nxdkcuzxIYh9cpM0NCuqRratb3O6Jyi2ChuFQAArl588UVJ0ogRI1yWz5gxQ5MmTVJYWJjWrl2r119/XWVlZcrMzNTIkSP1zjvvKD4+3pl++vTpCg8P14QJE1RTU6NRo0Zp5syZPr8D+eGHH9YNN9ygPXv2yOFw6IMPPtDGjRv1+uuv69///nez9s0wzUD+XSLwKioqlJiYqHX56xUf1/hhWHfY09/CrWlMyxKbZStreofbEvdtvGnO3+itpdo81MNfdst8g+lwmbfuqy/7GWY03WLmSx7uOVjq5ahznW9wnfekc5/eXtMAx6O0uk4LNu3XvI3FWnDkueWjbIbUt2Oi+uckqV92kvpnJ6pLhziF2WixQ/OYpqltB6qVv6NU+TtLtWLnQW3dX+2Spl1kmEb2TNOFfTI0rHt7JbXjjzQIHWPan+e2zGG5dbVZejVY11s7Pdg93PqGe7kvsd4HGm73gd7vt7xtY7338XavKUkRlnp7q2e45WBYt5c8Hb+m6xVmWW+9T2xc5ppnjaPBLY1LHXw4nvWWcirMH18vZzcblF+2QuXl5T4/SxtKjsYvfx1wiWLCIgJWbo29Xveu/qTVHbfPP/9cjz/+uPLz8+VwOHTaaafpD3/4g8vro/zBn4UBIICSYyM1fmBHjR/YUQ12h1YXlOm/R1qXfyiq1He7y/Xd7nJJOyVJsZFh6uMMmhPVPztJ2ckxdHOFR6ZpanNxlRZs3K+l20uUv7NUpYfcn8Hq0iFWp+el6Pxe6Tq3e3ueJQaAEBTKXaxDyYUXXuj3O5ObQoAMAEESHmbT4E4pGtwpRb+/6BTtLavR8h0Hj7xnuUzr9lSous6updsPaun2g87tOsRHaVBusgZ3StagvGT1zkpUZDjPMbdV+ytrlb+zVAs379eCjfu1p6zGZX1UuE39s5N0Wl6yBucl67S8ZLryA0ArQIAcHATIABAispJidOmAjrp0QEdJUoPdoS37q7SmoFzf7S7T2j3l2lBYof2VtfpsfZE+W984IEZUuE39c5I0OK8xaD4tN5lusiepBrtDPxRVauWuxi7TK3eVquCga0AcGW7TmV1SdW639hrUKVl9+AMKALRKBMje2Wy2JnvV2e3Wh0W9I0AGgBAVHmbTKRkJOiUjQRNOz5EkHa63a83ucq3YebDxudJdpSo7VK9l2w9q2U9ambulxWlwXmML86C8ZHVKjZWNZ5lbldLqOu0oqdaOkmpt2lellTtLtWZ3uWrqXX/sDUPqnhans7qkakTPNJ3ZJVUxkXSZBoDWjgDZuzlz5rjM19fXa9WqVXrttdf0yCOPNCtPAmQAaEWiI8J0RucUndE5RZLkcBwZhGnnQa04MhDTtgPV2lJcpS3FVXp7eYEkKT46XH07JqpfduOzzD3S49UptR2vmAqyertDG4sqtaW4StsPVB8JiA9px4Fqldd4fn9jfHS4BuYm67TcJJ2Wm6wBuUlKiA7cIC4AgMBphTFrQF166aVuy6688kr17t1b77zzjm6++Wa/8wzpALmhoUFTp07Vm2++qaKiImVmZmrSpEl68MEHZbNxUwcANpuhbmlx6pYWp6tPz5UklVQ1PpPaOGpxqdbuKVfl4QYt2lqiRVtLnNtGhtnUpUOseqTHKzelnbKTY9QxOUbZye2Um9KO0bNb0OF6u4rKD2tvWY12l9Zo/d7Gwdi+L6xQXYP7iK9HZSREq1P7durcPk4DchJ1Wm6yunaIozcAAABNGDJkiG655ZZmbRvSAfKf//xn/f3vf9drr72m3r17a8WKFbrxxhuVmJio3/zmN8GuHgCEpNS4KI3unaHRvTMk/dhKuXZP4+Bf6/dWaPO+KtXU253vZLaKiQhTn44JLi3O2ckxiqelskkOh6ldBw9p/d4KrdtbrvV7K5zPjR9LQnS4TslMUJf2scpLjVXn9u3UqX2s8lJi6SoNAG0YXaybp6amRs8995yys7ObtX1IB8iLFy/WpZdeqksuuUSS1KlTJ82ePVsrVqwIcs0AoPWICLOpT8dE9emYqGvPaGxldjhM7S6t0aZ9ldqyv0q7Sw9pT2lj6+bu0hrV1Nu1fEeplu8odckrqV2EspNjlJvSTl3ax6lLh1h1bt84JcZEtJnXT5mmqQNVddpTVqNt+6u0bk+F1u8t1/d7K1RZ6/n9njERYcpKilZWUoy6p8Wrf05jl/dOqe3azHEDAPiOANm75ORkl99Q0zRVWVmpdu3aadasWc3KM6QD5HPOOUd///vftWnTJvXo0UPfffedvvnmGz399NPH3Ka2tla1tT/+pb6ioiIANQWA1sVmM5Sb2k65qe10vtJd1jU+11yl7woaW5zX7CnXjgPVKj1Ur7Ij07o97tfWqHCb0hKilBYfrfQj/98hPkrpCY3/Hx1uU5jNkM1mKMww1C4yTAkxEUqMiVBUeNOjUJ5odQ0O7S2r0Z6yGpUdqlfl4XpVHm5QhfX/a+q1v6pWe8tqdLjec9foyDCbTsmMV++sBPXOSlSvrAR1To1VUru28wcEAMDxI0D2zhoX2mw2dejQQUOGDFFycnKz8gzpAPn3v/+9ysvLdcoppygsLEx2u12PPfaYrr322mNuM23atGaPWAYAOPpcc7y6pcXrikE/dk+qqm3QntIaFRw8pB0l1dp2oFrb9jcOLrWvola1DQ4VHKxxe+2QLyLDbEqICVdCdITiYyKUEB2u2MhwGUbjKM2GDB35nwzDkCHJZvz4bx1JExNpU1xUhOKjw5UQHS4ZhjPYrTxcr0O1dtXZHaprcKje7lB1nV17SmtUWF4jh583BoYhpcVHKS8lVr2yEpwBcff0OEUw+BkA4DgRIDetoaFBO3bs0E033aScnJwWyzekA+R33nlHs2bN0ltvvaXevXtr9erVuueee5SVlaWJEyd63Ob+++/X5MmTnfMVFRUtesAAoK2KiwpXz4x49cyId1t3uN6u/ZW1Kq48rOKKWu2rOKziylrtq2hctr+yVnV2hxwOU3bTlN1u6lC9XRU19XKYUp3doQNVdTpQVReEPWsUHWFTdnI7pbSLbAywYxoD7cYpojF4jw5XamykOibHKDMxhvcLAwBOGALkpoWHh+v//u//jhkXNjvfFs2thf32t7/Vfffdp2uuuUaS1LdvX+3cuVPTpk075oGIiopSVFRUIKsJAG1edESYclLaKSelnV/bmaap6rrGQLnicL0qahqc/66us0umKVNHbxJ+/LfjyK+4aUqmzCP/L9XU2Z2txZWHG2TKVHx0hDPIjY0MU2S4TZHhNkWE2RQdEaaOSTHKSYlRh7goukADAEKGqcC+5qmVxceSpFGjRmn+/PmaNGlSi+UZ0gHyoUOH3F7nFBYWJofj2K/EOBbjyHSsdd63NyzzTedhTe8L6xbWk7Q5t22m2bxj5TJvhFnmXVPYLBX1fH9p2cbL3jTvFtXyGVnrbQu3rHcvZeeGzZYlXj4Ftyx8qbm3T7YlPvmmGYbr9yq3Z5cWLwPwhWEYiosKV1xUuLIUE+zqACHpwtSRfm9jl+vvv/W+xNPvsMPy+xNm+Z20ti5Zf0btHpqfrOV4+xuUNQtrHTxxv2+xrrfuu/c8/E3RvPs+672lpZ6WHXF4OL7He//pKSCy3h+536WYlvWW88RjOU2fW27pfYjUrHnYzB/nHSfg3gmhacyYMbr//vu1bt06DRo0SLGxsS7rx40b53eeIR0g/+xnP9Njjz2m3Nxc9e7dW6tWrdJTTz2lm266KdhVAwAAAIAThi7W3v3617+WJD311FNu6wzDkN1u9zvPkA6Qn3vuOT300EO6/fbbVVxcrKysLN166636wx/+EOyqAQAAAMAJ4zDl9wCSx1tea9OcnsXehPToIvHx8Xr66ae1c+dO1dTUaOvWrXr00UcVGRkZ7KoBAAAAwAlztAU5kFNr8/rrr7u84veouro6vf76683KM6QDZAAAAABoi8wgTK3NjTfeqPLycrfllZWVuvHGG5uVZ0h3sQYAAACAtohnkL0zTdPjwLu7d+9WYmJis/IkQAYAAACAEEOAfGwDBw6UYRgyDEOjRo1SePiPYa3dbtf27dt10UUXNStvAmQAAAAAQKsxfvx4SdLq1at14YUXKi4uzrkuMjJSnTp10hVXXNGsvAmQAQAAACDEBPq54FbUgKyHH35YktSpUyddffXVio6ObrG8CZABAAAAIMQ4FODXPAWuqBYzceLEFs+TABkAAAAAQgzPIAcHATIAAAAAhCBi1sAjQAYAAACAEEMLcnAQIAMAAABAiCFADg4CZAAAAAAIMQ4FduCs1jhIl2maeu+99zRv3jwVFxfL4XDdiw8++MDvPAmQAQAAAACtzm9+8xu99NJLGjlypNLT02UYxnHnSYAMAAAAACGGLtbezZo1Sx988IEuvvjiFsuTABkAAAAAQoypwI5i3QrjYyUmJqpLly4tmqetRXMDAAAAABw30zQDPrU2U6dO1SOPPKKampoWy5MWZAAAAAAIMQzS5d1VV12l2bNnKy0tTZ06dVJERITL+pUrV/qdJwEyAAAAAIQYnkH2btKkScrPz9fPf/5zBukCAAAAgJMVzyB798knn+jzzz/XOeec02J58gwyAAAAAKDVycnJUUJCQovmSYAMAAAAACHGYQZ+am3++te/6ne/+5127NjRYnm2oS7WP3ZSMGTtm+5+Nrin8ZTfsXnf3pc8/EvvqVzT2+P2Hh42MC19920Ks8xbyvBhV6318vYJeP+EPG1jLcP17z+Gzfr3IPeKux8/s8n17hmEeVho+Qysx9zvZyU8pfd2bjRdxq6NO/ysg6cyrfOe/v7m7yfty5ng3/GzPpuS0yPPr+0BnBhj2p/nMm+9/np6Ns5uub66X4WavqZHeLhmeyvXesn29rvhib+/iR7z8PZzZPlxDvPhtybMy/XU27770uri7XfUl5/E462HpzKO976vObydB1a2Zjxb2ZxtvPF6nviQh/W7aa2n9f7Vml6SDMtHEvaTT948SdoA6WLt3c9//nMdOnRIXbt2Vbt27dwG6Tp48KDfebahABkAAAAAWodAv3qpNb7m6emnn27xPAmQAQAAACDE0ILs3cSJE1s8TwJkAAAAAAgxZoCfC26FDciSJLvdrg8//FAbNmyQYRjq1auXxo0bp7AwT48+ekeADAAAAAAhhhZk77Zs2aKLL75Ye/bsUc+ePWWapjZt2qScnBx98skn6tq1q995nhxPsAMAAAAA2pS7775bXbt2VUFBgVauXKlVq1Zp165d6ty5s+6+++5m5UkLMgAAAACEGPPIf4Esr7VZsGCBlixZopSUFOey1NRUPfHEEzr77LOblScBMgAAAACEmEC/m7g1vgc5KipKlZWVbsurqqoUGRnZrDzpYg0AAAAAIcYMwtTajB07Vr/61a+0dOlS52uxlixZottuu03jxo1rVp4EyAAAAAAQYo4GfIGcWptnn31WXbt21VlnnaXo6GhFR0fr7LPPVrdu3fTMM880K0+6WAMAAABAiHEcmQJZXmtimqbKy8s1e/Zs7d27Vxs2bJBpmurVq5e6devW7HwJkAEAAAAgxDBIV9NM01T37t21fv16de/e/biC4p+iizUAAAAAoFWx2Wzq3r27SkpKWjbfFs0NAAAAAHDcTDPwU2vz5JNP6re//a3WrVvXYnnSxRoAAAAAQgzPIHv385//XIcOHVL//v0VGRmpmJgYl/UHDx70O08CZAAAAAAIMTyD7N3TTz/d4nkSIAMAAABAiAl0t+fW0sV68uTJ+tOf/qTY2Fh17txZQ4cOVXh4y4W1PIMMAAAAACHGITPgU2vw3HPPqaqqSpI0cuTIZnWjbgotyAAAAAAQYswjUyDLaw06deqkZ599VqNHj5Zpmlq8eLGSk5M9ph02bJjf+RMgAwAAAECoMRvf9RvI8lqDv/zlL7rttts0bdo0GYahyy67zGM6wzBkt9v9zp8AGQAAAADQKowfP17jx49XVVWVEhIStHHjRqWlpbVY/gTIAAAAABBieM1T0+Li4jRv3jx17ty5RQfpIkAGAAAAgBDDa568Gz58eIvnSYAMAAAAACGGQbqCgwAZAAAAAEKMaZoBHaQroAOChbA2GiBbeth7OhkMb6+IbokTyJKHpR7uJXh6MsBwnbPU2zStI7cZlnkP+2Fa83RNY7PkYc3B05Gxlmqdbw5vn4D1WBjNON3d69l0zQ3Dfb1pWs6l49x5T5ub1vPA70ybUynLNtbvkac8rR+aWxpv9WiJM8c1j4KNOzwk8XPf3NZ7un74e81ozr5ay7DWoznf1hPP6/nrdh2T22fgcNS5bmKLcpm3XrdKN33pluWWl/7lMr9otes1d011mct8rZngWqbc38NYZXOte4LD9ToUY3OdjzZc52st+x5rc7+OHXb4N0KnzXLsGkzvT50Zbtd9/38D7ZbvibUezSnTmsZ6TodZyrD7UG23PA3/9tW9Tp7SeNvGtUxPh8pbOd7uYjxtbz3G3spw2w8fLh/+XmE81cHbL4fX+4NmfEbuV9em74V8YS3Der5aeSrD+j1ymNbP0Fqm9/sFt59qt3vNpvfd0/ENsx4v69fMLQ9rnbwf4XCX315v34DWgWeQg+PkOHsAAAAA4CRiBuG/1uLll1/Wtm3bTkjebbQFGQAAAADQGv3mN7/R4cOH1bFjR40cOVIjR47Ueeedp9zc3OPOmwAZAAAAAEIMg3QdW1lZmZYsWaIFCxZo3rx5uuOOO3T48GHl5eXpvPPOcwbNWVlZfudNgAwAAAAAIcZhmm7PlZ/o8lqLiIgInXvuuTr33HP14IMPqr6+XkuWLNG8efM0f/58zZ49W7W1tWpoaPA7bwJkAAAAAAgxtCD7zm63q66uTrW1tc7AuHPnzs3KiwAZAAAAAEJMoAfOak2DdB0+fFiLFi3S/Pnz9dVXX2nFihXq0qWLhg0bpjvvvFPDhw9vVvdqyccAuaKiwu+MExISvCcCAAAAALihBfnYkpKSlJ6ernHjxuk3v/mNhg8frrS0tBbJ26cAOSkpyeP7XY/FMAxt2rRJXbp0aXbFAAAAAKCt4hnkY+vfv79Wr16tBQsWyDAM2Ww2jRgxQqmpqcedt89drN977z2lpKR4TWeapi6++OLjqhQAAAAAAJ4sXbpU1dXV+vrrrzVv3jw9+eSTuvbaa9WjRw+NGDFCw4cPb3arsk8Bcl5enoYNG+ZzRN6lSxdFRET4XRkAAAAAQGh3sZ42bZo++OAD/fDDD4qJidHQoUP15z//WT179vwxP9PUI488opdeekmlpaUaMmSI/va3v6l3797ONLW1tZoyZYpmz56tmpoajRo1Si+88IKys7O91iE2NlYXXXSRLrroIklSZWWlvv76a82dO1e33HKLqqqqmjWKtc2XRNu3b/eruXrdunXKycnxuzIAAAAAgB8H6Qrkf75asGCB7rjjDi1ZskRz585VQ0ODRo8ererqameaJ598Uk899ZSef/55LV++XBkZGbrgggtUWVnpTHPPPfdozpw5evvtt/XNN9+oqqpKY8eOld1u97kuDodDS5cu1QsvvKBnn31WL7/8sioqKpodjzKKNQAAAACEoFB9Kvizzz5zmZ8xY4bS0tKUn5+vYcOGyTRNPf300/rf//1fXX755ZKk1157Tenp6Xrrrbd06623qry8XK+88oreeOMNnX/++ZKkWbNmKScnR19++aUuvPDCY5a/fPly5zuPjwbW2dnZGjFihJ599lmNHDlSnTp1ata+NStAXrZsmebPn6/i4mI5HA6XdU899VSzKgIAAAAAaBSs1zxZ32AUFRWlqKioJrctLy+XJOeYVdu3b1dRUZFGjx7tks/w4cO1aNEi3XrrrcrPz1d9fb1LmqysLPXp00eLFi1qMkAeMmSIMjMzNWLECD311FMaMWKEunXr5t8OH4PfAfLjjz+uBx98UD179lR6errL6Nb+jHQNAAAAAPDMlBTIgaWPFmXtmvzwww9r6tSpx97ONDV58mSdc8456tOnjySpqKhIkpSenu6SNj09XTt37nSmiYyMVHJysluao9sfy4YNG1yed25JfgfIzzzzjF599VVNmjTpBFQHAAAAAOCQKUcAW5CPllVQUKCEhATncm+tx3feeafWrFmjb775xm2dtQHVNE2vjaq+pElLS9Nzzz2niRMnutRVamzNfv311z2u84VPg3S5bGCz6eyzz/a7IAAAAABAaEtISHCZmgqQ77rrLn388ceaN2+ey8jTGRkZkuTWElxcXOxsVc7IyFBdXZ1KS0uPmeZYnn/+eS1cuNBjAJyYmKivv/5azz33XNM7egx+B8j/8z//o7/97W/NKgwAAAAA4F0oj2JtmqbuvPNOffDBB/rqq6/UuXNnl/WdO3dWRkaG5s6d61xWV1enBQsWaOjQoZKkQYMGKSIiwiVNYWGh1q1b50xzLO+//75uu+22Y66/9dZb9d577/m8Pz/ldxfrKVOm6JJLLlHXrl3Vq1cvt/cdf/DBB82qCAAAAACgUSi/B/mOO+7QW2+9pY8++kjx8fHOluLExETFxMTIMAzdc889evzxx9W9e3d1795djz/+uNq1a6frrrvOmfbmm2/Wvffeq9TUVKWkpGjKlCnq27evc1TrY9m6dau6d+9+zPXdu3fX1q1b/dijH/kdIN91112aN2+eRo4cqdTUVAbmAgAAAIAWFqxRrH3x4osvSpJGjBjhsnzGjBnOsap+97vfqaamRrfffrtKS0s1ZMgQffHFF4qPj3emnz59usLDwzVhwgTV1NRo1KhRmjlzpsLCwposPywsTHv37lVubq7H9Xv37pXN5ndnaUnNCJBff/11vf/++7rkkkuaVSAAAAAAoGmh3IJs+jC8tmEYmjp1apMjYEdHR+u5557z+3nhgQMH6sMPP9SZZ57pcf2cOXM0cOBAv/I8yu8AOSUlRV27dm1WYQAAAAAA70I5QA62O++8U9dcc42ys7P161//2tnibLfb9cILL2j69Ol66623mpW33+3OU6dO1cMPP6xDhw41q0AAAAAAQNNM0wz41FpcccUV+t3vfqe7775bKSkpGjhwoE477TSlpKTonnvu0eTJk3XllVc2K2+/W5CfffZZbd26Venp6erUqZPbIF0rV65sVkUAAAAAAPDFY489pksvvVRvvvmmtmzZItM0NWzYMF133XU644wzmp2v3wHy+PHjm10YAAAAAMA7ulh7d8YZZxxXMOyJ3wHyww8/3KIVCBTTdMg0HcdY677csJ4hltG63fOyjubtfop5T2FZYinD08hyhluu3kYVt5bhIYl15y31MAzrqHK+7HvT9fKz1j5yzdUwrE8UeCjV+rm6jdLu/6jt7gO9+7u33ssMibHkfRnRPhRHvXc7Lzyl8VLvFjhPPGRqmff4ZW3hMloiT/95+367X3PctzIM195Mx7ra/7i5e4qwCMu8l0NRowqX+WgPaQy3erpmard0Z3NYrr/W674v10JvaRym9zyP/8zylIPlWHjJw8vPsCQpTNbfZst137Leh18BD1dg/36/fDme3vO07of3PN3WWxL4kqfb76aXMnzhXu6Jv8Z4L8P7N8nmdt/X9Hrr98oX1jys3PfD/3p740utvf1KWrvlOnzI1Vstbdbvrof9sl5fw12+/yF4v9EMoTyK9cnM7wAZAAAAAHBi0YIcHD4N0pWSkqIDBw74nGlubq527tzZ7EoBAAAAQFtmBuE/+NiCXFZWpv/85z9KTEz0KdOSkhLZ7fbjqhgAAAAAAIHkcxfriRMnnsh6AAAAAACOoIt1cPgUIDscXoc7AQAAAAC0EAbp8m7gwIFuA2BKjYNiRkdHq1u3bpo0aZJGjhzpc54+PYMMAAAAAEAoueiii7Rt2zbFxsZq5MiRGjFihOLi4rR161adfvrpKiws1Pnnn6+PPvrI5zwZxRoAAAAAQgxdrL07cOCA7r33Xj300EMuyx999FHt3LlTX3zxhR5++GH96U9/0qWXXupTnrQgAwAAAEAIMgM4tUbvvvuurr32Wrfl11xzjd59911J0rXXXquNGzf6nCcBMgAAAACEnECGx60zTI6OjtaiRYvcli9atEjR0dGSGsfTioqK8jlPulgDAAAAQIihi7V3d911l2677Tbl5+fr9NNPl2EYWrZsmV5++WU98MADkqTPP/9cAwcO9DnPZgXIW7du1YwZM7R161Y988wzSktL02effaacnBz17t27OVkCAAAAAI4wjcYpYOUFrqgW8+CDD6pz5856/vnn9cYbb0iSevbsqX/84x+67rrrJEm33Xabfv3rX/ucp99drBcsWKC+fftq6dKl+uCDD1RVVSVJWrNmjR5++GF/swMAAAAAoFmuv/56LV68WAcPHtTBgwe1ePFiZ3AsSTExMc7u1r7wO0C+77779Oijj2ru3LmKjIx0Lh85cqQWL17sb3YAAAAAADRLWVmZs0v1wYMHJUkrV67Unj17mpWf312s165dq7feestteYcOHVRSUtKsSgAAAAAAfmQe+S+Q5bU2a9as0fnnn6/ExETt2LFDv/zlL5WSkqI5c+Zo586dev311/3O0+8W5KSkJBUWFrotX7VqlTp27Oh3BQAAAAAA8NfkyZM1adIkbd682aUb9ZgxY7Rw4cJm5el3gHzdddfp97//vYqKimQYhhwOh7799ltNmTJFv/jFL5pVCQAAAADAj3jJk3fLly/Xrbfe6ra8Y8eOKioqalaefgfIjz32mHJzc9WxY0dVVVWpV69eGjZsmIYOHaoHH3ywWZUAAAAAAPzI1I8jWQdkCvYON0N0dLQqKirclm/cuFEdOnRoVp5+B8gRERF68803tWnTJr377ruaNWuWfvjhB73xxhsKCwtrViWasmfPHv385z9Xamqq2rVrpwEDBig/P7/FywEAAAAAtB6XXnqp/vjHP6q+vl6SZBiGdu3apfvuu09XXHFFs/Js1nuQJalr167q2rVrczf3SWlpqc4++2yNHDlS//nPf5SWlqatW7cqKSnphJYLAAAAAMEU8PcgB7CslvJ///d/uvjii5WWlqaamhoNHz5cRUVFOuuss/TYY481K0+fAuTJkyf7nOFTTz3VrIp48uc//1k5OTmaMWOGc1mnTp1aLH8AAAAAQOuUkJCgb775Rl999ZVWrlwph8Oh0047Teeff36z8/QpQF61apXLfH5+vux2u3r27ClJ2rRpk8LCwjRo0KBmV8STjz/+WBdeeKGuuuoqLViwQB07dtTtt9+uW2655Zjb1NbWqra21jnvqU86AAAAAISyxoGzAvmap9brvPPO03nnndciefkUIM+bN8/576eeekrx8fF67bXXlJycLKmxK/SNN96oc889t0UqddS2bdv04osvavLkyXrggQe0bNky3X333YqKijrmiNnTpk3TI4880qL1AAAAAIBAMiQZCly/59bSw/rZZ5/1Oe3dd9/td/6GaZp+/bGgY8eO+uKLL9S7d2+X5evWrdPo0aO1d+9evytxLJGRkRo8eLAWLVrkXHb33Xdr+fLlWrx4scdtPLUg5+TkaN2KNYqPiz9GSQ63JYZ1/DLD9ZQxTes23k8pawr3A29ZYinD01+QrF8aw3AdKM1h1jddKU+fvqWiNiPCdRNLGdYsfKmnv6PDuX9C7qzH1/0T8eEzs36uhvdcvbMeD295+JseocfTZ+bv32W9XzG8p/ElD295nnjevpmea+S6L9Zrst1wvcqEWXIp3fiFW47bZ37iMr9ohWueq6rKXObLLIczWg1ueVbbXJclmJEu8xGWq2GMzfXv1nWm3WU+1uZ6PZakWodrGm+fsvV42j1es63zlt9AL6V4uqFrsHxGYW7XV1e+/La4bWNJYrOU4bAk8FSHQHxT/b3hdfjwGXnbxu1+wcM2x/vr47me1t//47/GWMuxfr+9fYa+3KdYzw27l3PHem75wnp+Nud7F240fUflLU9v30NJCne717TeE7vWq8HTHa4ljafrjst6S3rrsfKUpspR92MdzAYtKF2k8vJyJSQkNFlWKKqoqFBiYqL6Jg9WmK3ZQ0b5ze5o0NrSFSF/3Dp37uwyv3//fh06dMg5TlVZWZnatWuntLQ0bdu2ze/8/T7iFRUV2rdvn1uAXFxcrMrKSr8r0JTMzEz16tXLZdmpp56q999//5jbREVFKSoqym25aTbINBtvVNwvOB7CL+sX0S0gdivBmoHXFF4DYB8uttaH6f3/2fFQhiVT07D+yHrjnsL70fD3Rt/Tj78r64+w2x89PPHyQ+Nt7z3fGPn7qQQ+QPHlhs7bNi1R69YZ3vnG9HJz6j2o8eVb5O0m8fj/kNecz8j7udR0rp63t178wixrmz6itogYTxVpkvWaYzdcg1+Hh+1tXvK0W35/rDd83uYl9xtHaxrr8W2JLnTN+f5bf1a93fz79kviLVBvug4ngqcijrcFqDlbeyvT8/rjOzsC2dLlj+b8lnjLw8pTAGdlDaJPxNHy9kcg63fG5uHeyN8uvT41GVn/GOCliHBLeo93Z5aCoowfwxpv197WwjjyXyDLaw22b9/u/Pdbb72lF154Qa+88orz8d+NGzfqlltu8fh+ZF/4/Zqnyy67TDfeeKPee+897d69W7t379Z7772nm2++WZdffnmzKnEsZ599tjZu3OiybNOmTcrLy2vRcgAAAAAgtDTnZcbHMbWSAPmnHnroIT333HPO4FiSevbsqenTp+vBBx9sVp5+tyD//e9/15QpU/Tzn//c+b6p8PBw3XzzzfrLX/7SrEocy//8z/9o6NChevzxxzVhwgQtW7ZML730kl566aUWLQcAAAAAQkmgQ9bWFx5LhYWFzpj0p+x2u/bt29esPP1uQW7Xrp1eeOEFlZSUaNWqVVq5cqUOHjyoF154QbGxsc2qxLGcfvrpmjNnjmbPnq0+ffroT3/6k55++mldf/31LVoOAAAAAKB1GTVqlG655RatWLHC+az7ihUrdOuttzb7VU/Nfuo7NjZW/fr1a+7mPhs7dqzGjh17wssBAAAAgNBBG7I3r776qiZOnKgzzjhDERGNA1k2NDTowgsv1Msvv9ysPP0OkEeOHOk2et1PffXVV82qCAAAAACgkWEGeJAu6+i/rUCHDh306aefavPmzdqwYYNM09Spp56qHj16NDtPvwPkAQMGuMzX19dr9erVWrdunSZOnNjsigAAAAAAjqIF2Vfdu3dX9+7dWyQvvwPk6dOne1w+depUVVVVHXeFAAAAAKCt4zVPnk2ePFl/+tOffB7/6v7779dvf/tbpaSk+JTe70G6juXnP/+5Xn311ZbKDgAAAADaMCMIU+h75plndOjQIZ/T/+1vf1NZWZnP6Zs9SJfV4sWLFR0d3VLZAQAAAECbRQuyZ6ZpqkePHk2Oi/VT1dXVfuXvd4B8+eWXu8ybpqnCwkKtWLFCDz30kL/ZAQAAAAAsCJA9mzFjht/bpKen+5zW7wA5ISHBJVq32Wzq2bOn/vjHP2r06NH+ZgcAAAAAgE9O9MDQfgfIM2fOPAHVAAAAAAD8iFGsg8HvQbq6dOmikpISt+VlZWXq0qVLi1QKAAAAANo2I6D/ESA38rsFeceOHbLb7W7La2trtWfPnhapFAAAAAC0bbQgB4PPAfLHH3/s/Pfnn3+uxMRE57zdbtd///tfderUqUUrBwAAAABtEYN0BYfPAfL48eMlSYZhuD0YHRERoU6dOumvf/1ri1YOAAAAANoiAuSmNTQ0KDo6WqtXr1afPn1aLF+fA2SHwyFJ6ty5s5YvX6727du3WCUAAAAAAD8yZJPh/5BRx1VeaxIeHq68vDyPj/8eD7+Pwvbt2wmOAQAAAABB9eCDD+r+++/XwYMHWyxPn1qQn332Wf3qV79SdHS0nn322SbT3n333S1SMQAAAABo21pXt+dAe/bZZ7VlyxZlZWUpLy9PsbGxLutXrlzpd54+BcjTp0/X9ddfr+joaE2fPv2Y6QzDIEAGAAAAgOPEM8jeHR0nqyX5FCBv377d478BAAAAAC2PANm7hx9+uMXz9PsZ5D/+8Y86dOiQ2/Kamhr98Y9/bJFKAQAAAEBbdnSQrkBOaEaA/Mgjj6iqqspt+aFDh/TII4+0SKUAAAAAoG0zgjCFvpSUFB04cECSlJycrJSUlGNOzeHza56OMk1ThuF+8L777rtmVwIAAAAA8CNe8+TZ9OnTFR8fL0l6+umnWzx/nwPk5ORkGYYhwzDUo0cPlyDZbrerqqpKt912W4tXsMWYpmQ2vsvZtAb4pulhA4drEnlK8yNrn33Tsr2vaVyr5bre0x8m3LbxUGrTKXzI03R9t5hhhHnJwZclbqV4TeFvDg7LEpvb8Xfn7dkLb0fPl73w/xM48Zpz9L1t4/n4Hl8eno5Vc+rhLc/jdSKOZ3O2OTF5Nv2peL5WNn2Urd9Vay08/WB7+757ZbjnGR7pWs8wS7Xd9sJwvTaaZpg1hRtrqXbL74/DsMxb9svbb5EnJ+Ka4+088FSG+2/g8V/33a7Zhn95er7mW4/5ycGXz926r+6/myf+aPhSz1D43WwJ1vPP/b7Flf9XV+98+Ux9uf90zdN/YUbT1xCP31XLfXLET6/rHq7xOHlMnDjR479bis8B8tNPPy3TNHXTTTfpkUceUWJionNdZGSkOnXqpLPOOqvFKwgAAAAAbc3RxsmAldcK//xUXl6uuXPnaseOHTIMQ126dNGoUaOUkJDQ7Dx9DpCPRuedO3fW0KFDFRER0exCAQAAAADHRhfrps2aNUt33nmnKioqXJYnJibq73//u66++upm5evTUaioqHBOAwcOVE1Njcuyn04AAAAAgONjBOG/1mLlypW68cYbNX78eK1atUo1NTU6dOiQVqxYoZ/97Ge64YYb9N133zUrb59akJOSkrw27x8dvMtutzeZDgAAAADgjU3NeOnQcZbXOjz33HMaP368Zs6c6bL8tNNO0+uvv65Dhw7pmWee0auvvup33j4FyPPmzfM7YwAAAABA8wS6Vbc1tSB/++23euGFF465/rbbbtPtt9/erLx9CpCHDx/uU2arV69uViUAAAAAAD8yZJMRwBG5DbP1tCDv3btXPXr0OOb6Hj16aM+ePc3K+7iPQnl5uV544QWddtppGjRo0PFmBwAAAADAMR06dEjR0dHHXB8VFaXDhw83K2+fR7G2+uqrr/Tqq6/qgw8+UF5enq644gq98sorzc0OAAAAAHAEXayb9vnnn7u8evinysrKmp2vXwHy7t27NXPmTL366quqrq7WhAkTVF9fr/fff1+9evVqdiUAAAAAAD9qDI8D+Zqn1hUgH30N8bE09x3SPh/xiy++WL169dL333+v5557Tnv37tVzzz3XrEIBAAAAAMdmGLaAT62Fw+HwOjX37Uo+tyB/8cUXuvvuu/XrX/9a3bt3b1ZhAAAAAADv6GIdHD7/meDrr79WZWWlBg8erCFDhuj555/X/v37T2TdAAAAAKCNsgVhgs9H4ayzztI//vEPFRYW6tZbb9Xbb7+tjh07yuFwaO7cuaqsrDyR9QQAAACANoMu1sHh91Fo166dbrrpJn3zzTdau3at7r33Xj3xxBNKS0vTuHHjTkQdAQAAAAA44Y7rzwQ9e/bUk08+qd27d2v27NktVScAAAAAaOOMgP4nnkGWdBzvQf6psLAwjR8/XuPHj2+J7AAAAACgTTNkC/BrnuhiLbVQgAwAAAAAaDmBfi64tTyDnJyc7PM7jg8ePOh3/gTIAAAAABBiQv01TwsXLtRf/vIX5efnq7CwUHPmzHHpUTxp0iS99tprLtsMGTJES5Yscc7X1tZqypQpmj17tmpqajRq1Ci98MILys7OPma5Tz/9tF/19BcBMgAAAACEGsPWOAWyPD9UV1erf//+uvHGG3XFFVd4THPRRRdpxowZzvnIyEiX9ffcc4/+9a9/6e2331ZqaqruvfdejR07Vvn5+QoLC/OY58SJE/2qp78IkAEAAAAgxIT6M8hjxozRmDFjmkwTFRWljIwMj+vKy8v1yiuv6I033tD5558vSZo1a5ZycnL05Zdf6sILL/SpHlu3btWMGTO0detWPfPMM0pLS9Nnn32mnJwc9e7d2699kngbNAAAAACEHMMwAvwe5MYu1hUVFS5TbW1ts/dh/vz5SktLU48ePXTLLbeouLjYuS4/P1/19fUaPXq0c1lWVpb69OmjRYsW+ZT/ggUL1LdvXy1dulQffPCBqqqqJElr1qzRww8/3Kw6EyADAAAAACRJOTk5SkxMdE7Tpk1rVj5jxozRm2++qa+++kp//etftXz5cp133nnOgLuoqEiRkZFKTk522S49PV1FRUU+lXHffffp0Ucf1dy5c126b48cOVKLFy9uVr3pYg0AAAAAIaaxBTmAg3QdKaugoEAJCQnO5VFRUc3K7+qrr3b+u0+fPho8eLDy8vL0ySef6PLLLz/mdqZp+rzfa9eu1VtvveW2vEOHDiopKfG/0qIFGQAAAABCkC0Ik5SQkOAyNTdAtsrMzFReXp42b94sScrIyFBdXZ1KS0td0hUXFys9Pd2nPJOSklRYWOi2fNWqVerYsWOz6tlmWpBNh12mw35kzoe/SNisaUzXWctfNSxrJdNtiUy3LN3TuBZh3cC93taH6T2lcCnSbbWHOlgXWetpOty3+WmWPo2AZy3Ey/H2sGemJY11aHq33fCy/bHKaWobb2VIks2tXk3vm+d6/TR1c45F0+sdHsp0P/ua3saXv/N5O17e8vB0ZPw9k6ya83dZb69B8PYZNqbxb731PPJcjvfP9dipfauHtxS+nEvuOVi+V9ZLjocMrOVY592/d67Cotq55Rke7TpapkN2l3m3PEzv313ruWKdb7CU4TCb3i/vn4f388/bedHIv+uStd6BavDw5bt2vNtbd8X9+LXMN8k1Ry+/3R5K9fYb5+13wRN/j69v6b1dP1uezXJCWs/XxnK9/Ta7suZhLcOXeljLtOYZ1owvkrd6+HJ8vX2fPR0/lzI8rPdWL+s126cWPMv9ZkQT61qrk+09yCUlJSooKFBmZqYkadCgQYqIiNDcuXM1YcIESVJhYaHWrVunJ5980qc8r7vuOv3+97/XP//5TxmGIYfDoW+//VZTpkzRL37xi2bVs80EyAAAAADQWoT6e5Crqqq0ZcsW5/z27du1evVqpaSkKCUlRVOnTtUVV1yhzMxM7dixQw888IDat2+vyy67TJKUmJiom2++Wffee69SU1OVkpKiKVOmqG/fvs5Rrb157LHHNGnSJHXs2FGmaapXr16y2+267rrr9OCDD/q1P0cRIAMAAABAqAnx9yCvWLFCI0eOdM5PnjxZUuN7il988UWtXbtWr7/+usrKypSZmamRI0fqnXfeUXx8vHOb6dOnKzw8XBMmTFBNTY1GjRqlmTNnHvMdyEeNHz9ev/zlL3XxxRfrzTff1J/+9CetXLlSDodDAwcOVPfu3f3al58iQAYAAACAEBPqXaxHjBjhsUv9UZ9//rnXPKKjo/Xcc8/pueee86vsmpoajR8/XmlpaZo0aZJuuukmXXnllX7lcSwnRwd9AAAAAECb8Pnnn2vHjh369a9/rXfffVc9e/bUsGHD9Prrr6umpua48iZABgAAAIAQc/Q1T4GcWpPs7Gw99NBD2rJli7788kvl5eXp9ttvV0ZGhm699VYtXbq0WfnSxRoAAAAAQk2IP4McSkaOHKmRI0eqsrJSb731lh544AG98soramho8DsvAmQAAAAACDGGbG6vdD3R5bVm27Zt08yZMzVz5kyVl5f7PBK2FQEyAAAAAISYQHd7bm1drKXGwbr++c9/asaMGVq4cKFyc3P1y1/+UjfeeKNycnKalScBMgAAAACEGsMIcBfr1hMgL1q0SDNmzNC7776ruro6jR8/Xp9//nmzW41/igAZAAAAAEIMLcjHds4556h///567LHHdP311ys5ObnF8iZABgAAAAC0GitWrNBpp512QvImQAYAAACAUMMo1sd0ooJjiQAZAAAAAEIOXayDgwAZAAAAAEINLchBQYAMAAAAACHHCPDI0rQgSwTIAAAAABByDMMmI4CtuoEsK5QRIAMAAABAqDEC3ILcSp5BHjhwoM/PS69cudLv/AmQAQAAAACtwvjx409o/gTIAAAAABBi6GLt2cMPP3xC8ydABgAAAIBQQxfroCBABgAAAIBQQ4Dsld1u1/Tp0/Xuu+9q165dqqurc1l/8OBBv/NsHe3oAAAAANCGGIYR8Km1eeSRR/TUU09pwoQJKi8v1+TJk3X55ZfLZrNp6tSpzcqTABkAAAAAQo3NkGy2AE6tL0B+88039Y9//ENTpkxReHi4rr32Wr388sv6wx/+oCVLljQrzzbTxdp0NMh0NDTOWP46YhjeD4Npmq7bWNZbH2p3mHa3PAwjzEsp1lxNy2oPJ631YXq3h+sdrqstWXp6IbhpOCwLTMusZd+s9XIrQ+5/kTK97ZvrvOkhU+sSaxprjpa98pint8uCexne62kt11ue3ng+Fk3vu/dj4/1YeNvGPb33i6y1XtZatEQe7umtZbjzVq57md4/Q//z9M76Gbh/+72dF955S+PtXPOFw8smvpzzDus12stlyRYe5ZZneIzrEfT2F2Sb6f38tFkKdlguwtbPyG65Ylj3y269dsq37/NPecjCPY313LEU4qkeP2Xz9Nvi07evqfSB4fUa4rYbvtSz6d8K9+uDL9eYpvO01tOH09Xvz8htew/VdLsOGU1fl1qC+/Wh6To11sO1JtbvnrfPzJq+cRtXYV5a59zva7yzpvFUj5+yGdbzxp29Gb9H/rIeT+v11nrfaL0P97RN1E/ugW2tZLApr+hi7VVRUZH69u0rSYqLi1N5ebkkaezYsXrooYealedJcvYAAAAAwMmDLtbeZWdnq7CwUJLUrVs3ffHFF5Kk5cuXKyrK/Y/hviBABgAAAAC0Opdddpn++9//SpJ+85vf6KGHHlL37t31i1/8QjfddFOz8mwzXawBAAAAoNWgi7VXTzzxhPPfV155pXJycvTtt9+qW7duGjduXLPyJEAGAAAAgFBDgOzVwoULNXToUIWHN4a1Q4YM0ZAhQ9TQ0KCFCxdq2LBhfudJF2sAAAAACDWGLfBTKzNy5EiP7zouLy/XyJEjm5UnLcgAAAAAEGpshoxAvnrJl+HuQ4xpmh4HFyspKVFsbGyz8iRABgAAAIBQQxfrY7r88sslNY70PWnSJJcRq+12u9asWaOhQ4c2K28CZAAAAAAINQTIx5SYmCipsQU5Pj5eMTExznWRkZE688wzdcsttzQrbwJkAAAAAECrMWPGDElSp06dNGXKlGZ3p/aEABkAAAAAQk6AW5DVelqQj3r44YdbPM/WN1QZAAAAAJzsjnaxDuTUyuzbt0833HCDsrKyFB4errCwMJepOWhBBgAAAIAQYwR4FOuAjpjdQiZNmqRdu3bpoYceUmZmpscRrf1FgAwAAAAAoYZBurz65ptv9PXXX2vAgAEtlicBMgAAAACEGgJkr3JycmSaZovmyTPIAAAAABBqeAbZq6efflr33XefduzY0WJ50oIMAAAAAGh1rr76ah06dEhdu3ZVu3btFBER4bL+4MGDfudJgAwAAAAAocZQYN+81PoakPX000+3eJ4EyAAAAAAQamxG4xTI8lqZiRMntniereoZ5GnTpskwDN1zzz3BrgoAAAAAnDg8g+wTu92u999/X48++qgee+wxzZkzR3a7vdn5tZoW5OXLl+ull15Sv379gl0VAAAAADixDAV4FOvAFdVStmzZoosvvlh79uxRz549ZZqmNm3apJycHH3yySfq2rWr33m2ihbkqqoqXX/99frHP/6h5OTkJtPW1taqoqLCZQIAAACA1sQwjIBPrc3dd9+trl27qqCgQCtXrtSqVau0a9cude7cWXfffXez8mwVAfIdd9yhSy65ROeff77XtNOmTVNiYqJzysnJCUANAQAAAKAFHX0GOZBTK7NgwQI9+eSTSklJcS5LTU3VE088oQULFjQrz5APkN9++22tXLlS06ZN8yn9/fffr/LycudUUFBwgmsIAAAAAAi0qKgoVVZWui2vqqpSZGRks/IM6QC5oKBAv/nNbzRr1ixFR0f7tE1UVJQSEhJcJgAAAABoVYwgTK3M2LFj9atf/UpLly6VaZoyTVNLlizRbbfdpnHjxjUrz5AepCs/P1/FxcUaNGiQc5ndbtfChQv1/PPPq7a2VmFhYT7lZTrsMh2No5kZNss2hum+gaUPvtECZ4w1D9Oap1sRrn+/MAz3fTUM1zTWPA1LHqYPu2E9HA6zwZLC4SUHT+utdbfW0/oZWI+VO9N0XWp9bMK6jWlZYnr/2D2V2nQZHjI1PJ1fTbJWwt/tPW1hrbfrvMNDEdZeNl731S2993q7ZeE1D/cPyOt5YMnCe46e8mi6DA85uC3xfjy8fCYeynS4HUAv53gzLmPejp/beeJpN92+A64bOSy5+vIN8Pb9th4/X56rCo+JsNTrsMt8hOV6G2Far7/uNbVeDa0prOvtlhTWeeuxkqQwyxGze/wQjs1Tvd0+Z9O/z6zB9PY74f0v9NYybB5OYGuaMKPp3w7rsfF0Xnj7rXD7DHw43GGWYqz18PdYeMrT33p6+kpY62Utw8prmfL03bTsu9tnZj23vH933a8HXuZ9uE5Z76fcz3nrfYx37se36TKsPK21Hk9vl31reus1xlMenu5tXNP78HvvNYUra73DDP/a9Byt8FlajwI9snQrPG7PPvusJk6cqLPOOksREY2/4w0NDRo3blyz35Ec0gHyqFGjtHbtWpdlN954o0455RT9/ve/9zk4BgAAAIBWhQDZq6SkJH300UfasmWLNmzYINM01atXL3Xr1q3ZeYZ0gBwfH68+ffq4LIuNjVVqaqrbcgAAAAA4eQT63cStL0BeuHChTjnlFHXr1s0lKK6vr9fixYs1bNgwv/MM6WeQAQAAAKBNsgVhamVGjBih/v37a/HixS7LDx48qJEjRzYrz5BuQfZk/vz5wa4CAAAAAJxYdLH2yTXXXKNRo0bphRde0KRJk5zLvT0/fyyt8O8EAAAAAIC2zjAM3X///Zo1a5buuusuTZ482RkY+zJApycEyAAAAAAQao62IAdyamWOBsOXX365Fi5cqPfee09jxoxRWVlZs/MkQAYAAACAUMN7kP0ycOBALVu2TGVlZRo1alSz8yFABgAAAIBQQwuyVxMnTlRMTIxzPiMjQwsWLNCoUaOUm5vbrDxb3SBdAAAAAHDSsxmNUyDLa2VmzJjhtiwqKkqvvfZas/MkQAYAAACAUBPobs+tJD5es2aN+vTpI5vNpjVr1jSZtl+/fn7nT4AMAAAAAKGG1zx5NGDAABUVFSktLU0DBgyQYRgur3Q6Om8Yhux2u9/5EyADAAAAQKihBdmj7du3q0OHDs5/tzQCZAAAAABAq5CXlydJqq+v19SpU/XQQw+pS5cuLZY/o1gDAAAAQIgxDCPgU2sSERGhOXPmtHi+BMgAAAAAEGqOjmIdyKmVueyyy/Thhx+2aJ50sQYAAACAUMMzyF5169ZNf/rTn7Ro0SINGjRIsbGxLuvvvvtuv/MkQAYAAACAUMMo1l69/PLLSkpKUn5+vvLz813WGYZBgAwAAAAAJwVakL1iFGsAAAAAaAtoQfbL0XchH+9gYwzSBQAAAABolV5//XX17dtXMTExiomJUb9+/fTGG280Oz9akAEAAAAg1BgKbHNmK2xAfuqpp/TQQw/pzjvv1Nlnny3TNPXtt9/qtttu04EDB/Q///M/fudJgAwAAAAAoYYu1l4999xzevHFF/WLX/zCuezSSy9V7969NXXqVAJkAAAAADgpMEiXV4WFhRo6dKjb8qFDh6qwsLBZefIMMgAAAACEmqMtyIGcWplu3brp3XffdVv+zjvvqHv37s3Ks+20IBu2xulY66yLDNdDY5r1lhRm08V5KsvtpDMsq8O85OHppHVd5rDUK8yap5d6S+57Zhh21/Wmo8kNPI8cZ1gTWdaHyV/Wepred82yvfsGNtOaxr9aeMrTMA2vaVzSu5VgWta7H1/3NE2vd9/ewzK3hdZ9dWXzcz99qYj7vrrn6VaOl2Ldzm8PaQy3LC3H18u+errSeCvX+t11S+9hv+zW76KlZGuenvKwsh5z6765nxZNX4M8lWs9Pg6z6fPXYVj3U3JY8rRbT1jrJcdSr/DoBLc8o3M6uKYxqlznLTUPcztW7meTQ67XT/fPzLVe1mNhPZ4Nbtu7X+c9fQauJXq/pvibh9v1wMM27ud40zdibtd4j9//pheEWYrw5Xh6Y5frNp6Onxvr+WrJw3osfLl6ml5+9KxrrWVar9key/V2LfRyD+JpG8l6D9H0dcuXlhzTcpFxuxxYdtV6LCT367rN8HKuuF1jmhFYeDm+1nnr9cFTPfwV5umabZm3e/sd8JJeksK8/r4fv/CffNCOVhjoeUQLslePPPKIrr76ai1cuFBnn322DMPQN998o//+978eA2dftJ0AGQAAAABaCwJkr6644gotXbpU06dP14cffijTNNWrVy8tW7ZMAwcObFaedLEGAAAAAPhl4cKF+tnPfqasrCwZhqEPP/zQZb1pmpo6daqysrIUExOjESNGaP369S5pamtrddddd6l9+/aKjY3VuHHjtHv3br/qMWjQIM2aNUv5+flauXKlZs2a1ezgWCJABgAAAIDQYzMCP/mhurpa/fv31/PPP+9x/ZNPPqmnnnpKzz//vJYvX66MjAxdcMEFqqysdKa55557NGfOHL399tv65ptvVFVVpbFjx8put3vM86iKigqfpuagizUAAAAAhJogdbG2BpZRUVGKiopySz5mzBiNGTPGY1amaerpp5/W//7v/+ryyy+XJL322mtKT0/XW2+9pVtvvVXl5eV65ZVX9MYbb+j888+XJM2aNUs5OTn68ssvdeGFFx6zqklJSccY9+jH8g3D8Bpoe0KADAAAAAAhxjCMJoPAE1GeJOXk5Lgsf/jhhzV16lS/8tq+fbuKioo0evRo57KoqCgNHz5cixYt0q233qr8/HzV19e7pMnKylKfPn20aNGiJgPkefPmOf9tmqYuvvhivfzyy+rYsaNf9fSEABkAAAAAQk2QWpALCgqUkPDj2x48tR57U1RUJElKT093WZ6enq6dO3c600RGRio5OdktzdHtj2X48OEu82FhYTrzzDPVpUsXv+tqRYAMAAAAAKEmSAFyQkKCS4B8XFlaWsCPdn1uii9pTiQG6QIAAACAUGOo8UXeAZtaruoZGRmS5NYSXFxc7GxVzsjIUF1dnUpLS4+ZJhgIkAEAAAAALaZz587KyMjQ3Llzncvq6uq0YMECDR06VFLj65kiIiJc0hQWFmrdunXONP5oqVZnulgDAAAAQKgJUhdrX1VVVWnLli3O+e3bt2v16tVKSUlRbm6u7rnnHj3++OPq3r27unfvrscff1zt2rXTddddJ0lKTEzUzTffrHvvvVepqalKSUnRlClT1LdvX+eo1sdydGTsow4fPqzbbrtNsbGxLss/+OAD/3ZKBMgAAAAAEHqa8W7i4y7PDytWrNDIkSOd85MnT5YkTZw4UTNnztTvfvc71dTU6Pbbb1dpaamGDBmiL774QvHx8c5tpk+frvDwcE2YMEE1NTUaNWqUZs6cqbCwsCbLTkxMdJn/+c9/7lfdm0KADAAAAAChJsRbkEeMGCHTNI+dnWFo6tSpTb4iKjo6Ws8995yee+45v8qeMWOGX+n9QYAMAAAAAKEoeIM5t1kEyAAAAAAQYgzDCOjrjoL5aqVQQoAMAAAAAKEmxLtYn6wIkAEAAAAg1BAgBwXvQQYAAAAAQLQgAwAAAEDoMYzGKZDlgQAZAAAAAEIOXayDggAZAAAAAEKMYWucAlkeCJABAAAAIPTQghwUBMgAAAAAEGp4BjkoCJABAAAAINTQghwUbSZANmxhMmxhx1jn3uHesHbCt8ybpsO6gTUH9zytb9WybGMYrvUzLXmYbjlKpmWpwzJvc6uH9zPfuu/Wck1HQ9PpTQ9l2Cz76nYsmq6TdT8l931128Zser3HbSz18DcPT6kNy1JvOVoOlRyWOljz85SnNY1bHpZzz5f99LaNaV3vKQ/LvLdS/T12vrAeC5uHv5b6+xm4fUc8nM/ejrHXffOQp90tT9frkrd9bYnz1Vova5kekridS3bL9TTMck1xeKhEg2Ubax7Wa0yYYTkWEdFueUZ0yLBss911G0u9ow3Xn9Aa0/XaKLkfP+t1y7q+wfIZ2i0prOslKcKyr9YyvH3/PX3K3j4T6/lsPTYNnq5TbtcMtyRN1spTcrfvnvX4WgqxHk9Pvy1Whqznq3Xfm66Tp3KseTgseVj31dP3ytO1q6l6WPMI83Sdcptv+ny13j1Zv5ceuR2vprfx9Bm51cPyObvdH5hNf0c85eotT7fzwEOOVtY8wi3fK2ue1u+Rx/PVuqte6mDNI8LDQ6feflus3wlrng2efgfcfn+avi5Z7189fWbWND/NgoZQHI82EyADAAAAQGtBD+vgIEAGAAAAgFBDF+ugIEAGAAAAgFBDE3JQECADAAAAQIghPg4OAmQAAAAACDV0sQ4KAmQAAAAACDUEyEHhy6j0AAAAAACc9GhBBgAAAIAQYxiGh3fXn9jyQIAMAAAAAKGJmDXgCJABAAAAIMQYtsYpkOWBABkAAAAAQg+DdAUFATIAAAAAhBoC5KAgQAYAAACAEMMgXcFBT3MAAAAAAEQLMgAAAACEHrpYBwUBMgAAAACEGMNonAJZHgiQAQAAACD00IIcFATIAAAAABBiaEEODgJkAAAAAAg1RMhBQYAMAAAAACGG+Dg4eM0TAAAAAACiBRkAAAAAQg4tyMFBgAwAAAAAoYZRrIOCABkAAAAAQgwtyMHRZgJkW3ikbOGRjTOmZZ0R4ZbetP4JxQhznbWkNyzrPf4Jxi0P14o4LNs43NY73PO0MC37ZjdMzwmPsHmop/u+uz6qbljmTdPR5PqjuVoSWdY3PW962HfTsrPWPfW23hPDyzbWWlo/I495uu1L09uYZtPngS+sZdqtn5HpvU7WPNxOLgtrPT0l93bhtW5jTd+cPK0clkwcpnsGNqPp4+PtM7V+ho1pmuatDE+s++Lvev/PLO/7avdQZpjleBqG9TOwbuP9Wmfdxu6wnOM21zIjLcfTCIt0r2dsguu85VJmvV6GW691Hg6odVGD9bpuORYNlu+q9Rru6fjWW46XNQ/rd9Pm4fy0crvumE1/JtZqebvOSe6fodt1y7Jfnn6v3PK0pAmzbGK9FlrTN9bDMm/Nw/oZWq8pHvY9zDLki3Xf3O5LvNwPSO6fo/X4uV2TfbhOWVnvB9x+Z70cG8/luh4Lm5dqeFrtfsVo+nxrcPuOePrNs8wbXrYxvJ9LVm7fC8ustZ7hlvPG0zXdeg3x9ttRb9pdyzDd79ms1xnDbcgi/+4HGrfw757M/fz1pZyTMLqjBTko2kyADAAAAACthWEYMgLYrBvIskIZATIAAAAAhJoAd7GmBbkRATIAAAAAhCKC1oDjPcgAAAAAAIgWZAAAAAAIOYxiHRwEyAAAAAAQYgiQg4MAGQAAAABCDAFycBAgAwAAAECoIUIOCgJkAAAAAAgxxMfBQYAMAAAAACGGADk4eM0TAAAAAACiBRkAAAAAQg4tyMFBgAwAAAAAIYYAOTgIkAEAAAAgxBAgBwcBMgAAAACEGuPIFMjyQIAMAAAAAKHGMAwZAWzWDWRZoYwAGQAAAABCDF2sgyOkX/M0bdo0nX766YqPj1daWprGjx+vjRs3BrtaAAAAAICTUEgHyAsWLNAdd9yhJUuWaO7cuWpoaNDo0aNVXV0d7KoBAAAAwAlztAU5kBNCvIv1Z5995jI/Y8YMpaWlKT8/X8OGDQtSrQAAAADgxKKLdXCEdIBsVV5eLklKSUk5Zpra2lrV1tY65ysqKk54vQAAAACgJREgB0dId7H+KdM0NXnyZJ1zzjnq06fPMdNNmzZNiYmJziknJyeAtQQAAACA40cX6+BoNQHynXfeqTVr1mj27NlNprv//vtVXl7unAoKCgJUQwAAAABoGUdf8xTICa2ki/Vdd92ljz/+WAsXLlR2dnaTaaOiohQVFRWgmgEAAABAyzMU4C7WgSsqpIV0gGyapu666y7NmTNH8+fPV+fOnZudl80WIZst0pmvCyPMLb1drmnCDUtju9vZarOsdm+cd1jmLbWQXXbX9JYEdtOagztrntZ5a61NH7514YbraWIarvV0z8FTntZlTZdrrbf1WDQuc13osGxl/Zibw7TkaVjq7bB8Jp7qabPsqjWN23rLZ2L3lKkX1jztzTgY1n21sh4bX4R5Od+8HRtP++Fvnu485Gkp2HrNsP6F1bre9PD993a8rJ+ztQ4O030/reefqabrZb1OeaqT35+7JXmDw8N1ymYp15LG7Rx3O3zueVp3zXpuGG7HxlKEh88oIqGDy3yU5RfSeo2xnnuejqd1mcOwzntJb6mm3e5+LOotH0KD5XhZt7D+4nmutyvrvtus10IPn5GVdRv33yfXJQ1uZXo/X615mJbvjfW33ZreE8O07qulDOtn5jEX16XWfXMr060Md+53Lk3Xy9v9QWO5rqz7amW9Ltk9fVfdt2oyD2918lQv67nlnof3erpd+yzXEOs21nPLUx3cj3nT33/rfU2tGprMT5JsPtTjp6znXp1pd0vjvizCZc7b726Dh/tV97tAL79XhvW77c66r3bT878Bf4V0gHzHHXforbfe0kcffaT4+HgVFRVJkhITExUTExPk2gEAAADAicEgXcER0s8gv/jiiyovL9eIESOUmZnpnN55551gVw0AAAAATigjgBMahXQLsnv3QAAAAAA4+dGCHBwhHSADAAAAQFtEgBwcBMgAAAAAEGJshiFbAKPWQJYVygiQAQAAACDE0IIcHCE9SBcAAAAAAIFCCzIAAAAAhBhakIODABkAAADA/2/v/oOjqu4+jn/uJtlswGSRoLusQEiVYoxUS2wDjDZ0xgLOFGn5owFbCmOdgXGcgrZYlE6NTAdox6aU2kCl1LEz7QMzBRyndUbSaUzSov2B+JiKVZQgIKQZbCXwSH7uef6IbL13N9lAkr0n5P1i7gz317nn3u/ePfvNuT9gGRJkf3CJNQAAAABY5mKCnMlhoKqqquQ4jmuIRqOJ+cYYVVVVKRaLKS8vT3PnztXrr78+DEdp6JEgAwAAAIBlvAloJoZLUVpaqtOnTyeGpqamxLwf/vCHqq6u1pNPPqm//e1vikaj+sIXvqBz584N9WEaclxiDQAAAACWsf0S6+zsbFev8UXGGG3ZskXr16/X4sWLJUnPPPOMIpGIfvOb32jlypVDUd1hQw8yAAAAAFgm4GR+kKS2tjbX0NHRkbJ+R44cUSwWU3FxsZYsWaKjR49Kkpqbm9XS0qJ58+Ylls3NzVVFRYUOHDgw7MdtsEiQAQAAAMAyft2DPHnyZIXD4cSwadOmpLqVl5frV7/6lV544QXt2LFDLS0tmjNnjt5//321tLRIkiKRiGudSCSSmGczLrEGAAAAAEiSTpw4oYKCgsR4bm5u0jJ33XVX4v8zZszQ7Nmzdf311+uZZ57RrFmzJCnpnmZjzCXf5+wHepABAAAAwDJ+9SAXFBS4hlQJstfYsWM1Y8YMHTlyJHFfsre3uLW1NalX2UYkyAAAAABgGceHf5ero6NDb7zxhiZOnKji4mJFo1HV1tYm5nd2dqq+vl5z5swZikMzrLjEGgAAAAAs8/EHZ2VqewP17W9/WwsXLtSUKVPU2tqq73//+2pra9Py5cvlOI7WrFmjjRs3atq0aZo2bZo2btyoMWPG6J577hm+HRgiJMgAAAAAYJsMv+bpUjqQT548qaVLl+rMmTO65pprNGvWLL388ssqKiqSJD388MO6cOGC7r//fv3nP/9ReXm59u/fr/z8/GGq/NAhQQYAAAAAy9j8HuRdu3alKctRVVWVqqqqBlcpH5AgAwAAAIBlAo6jQAYz5Exuy2Y8pAsAAAAAANGDDAAAAADWsfkS6yvZqEmQu+Wo+6M7z7Mcd8d5j0zS8j0m7hrPcrI8S3hefO2ZG09RpvFMixv3eJdnm91x97h3/d5a9P9JNo57He/LuQMDuBs/23F/TAIB97gxPWnL8J5xyfV2j3uPX6rj2WO8xzPuGXcvn+r4eZn0i1xymV3x/ud7nxjoPTbez8FApHox+6XMH+gyl7p8d5pvXu86WYH0xyI7cGkXwng/F6mXSXdepCsk/TnhPRTdnooZpTtnkr+nvJdGeUMQcNJ/p2R5ykh3HnmX99aptxBvPdzjSevEPTFNEeIeT8W8xy87aR3PORBIbv5C44tc43me1z56v7O9+55K3PMdHPeuYtxtS9J3tmeFbu/BlJI+jt2O97PkGfd8vlN93L3reD9/Ac+x6PHWO8Xn1fGs491uwLMf3jLjAyjTu11H/dczkPZcT1/GQHSnOV7p65Bcz1Tt4qVI/Z2e9AHtd35gIG219/No3Cen9/imq1Eq3t9x3t82xnPepKqn9xh7v4O9517yZy1Zcpn9/w5M+u2TdKySefc1XVvclbQfydpNt6debrnG+5vYrSfV95SHt97e38DetifV9623jODHGgvvsR2pHGU4Qc7cpqw2ahJkAAAAABgpuAfZHyTIAAAAAGAZR5nt1SU97kWCDAAAAACWCTjJtyUN9/ZAggwAAAAA1uEhXf7gNU8AAAAAAIgeZAAAAACwTm8Pcua6delB7kWCDAAAAACW4R5kf5AgAwAAAIBluAfZHyTIAAAAAGAZ3oPsDxJkAAAAALAQKWvmkSADAAAAgGW4B9kfvOYJAAAAAADRgwwAAAAA1nEcJ8OveaILWSJBBgAAAADrcIm1P0iQAQAAAMAy9CD7gwQZAAAAACwTUGYfGMXDqXqRIAMAAACAZehB9gcJMgAAAABYhnuQ/UGCDAAAAACWcZzeIZPbA5eaAwAAAAAgiR5kAAAAALBOwHEUyGC3bia3ZTMSZAAAAACwjPPRkMntgQQZAAAAAKxDD7I/SJABAAAAwDI8pMsfoyZBnnrDZBUUFPhdDQDACLH6f/e5x32qBwDg0rS1tel/wmG/qzFojjLbg+xwkbWkUZQgAwAAAMBIwT3I/uA1TwAAAAAAiB5kAAAAALAOD+nyBwkyAAAAAFiGh3T5gwQZAAAAACxDD7I/SJABAAAAwDLOR/8yuT2QIAMAAACAdQJO75DJ7YEEGQAAAACs4ziOnEy+B5lLrCXxmicAAAAAACTRgwwAAAAA1gkos72Z9Jz2IkEGAAAAAMtwibU/SJABAAAAwDI8pMsfJMgAAAAAYBle8+QPEmQAAAAAsE2GL7EWl1hLIkEGAAAAAOtwibU/eFgZAAAAAACiBxkAAAAArMM9yP4gQQYAAAAAyzhOZm8L5hbkXiTIAAAAAGAZepD9QYIMAAAAAJbhIV3+IEEGAAAAAMs4GX7NU0ZfKWUxEmQAAAAAsIyjzF72THrci9c8AQAAAAAgepABAAAAwDo8xdofJMgAAAAAYBmeYu0PEmQAAAAAsIyjzN4XTHrciwQZAAAAACzDU6z9QYIMAAAAAJahB9kfJMgAAAAAYBl6kP1BggwAAAAAlqEH2R+8BxkAAAAAANGDDAAAAADW4TVP/iBBBgAAAAALkbJmHgkyAAAAAFjGcXqHTG4PJMgAAAAAYCEe0+UHEmQAAAAAsAzpsT9IkAEAAADAMjykyx+85gkAAAAAAJEgAwAAAAAgiUusAQAAAMA63IPsDxJkAAAAALAM9yD7g0usAQAAAADQCEmQa2pqVFxcrFAopLKyMjU2NvpdJQAAAAAYNo4PA0ZAgrx7926tWbNG69ev16FDh3THHXforrvu0vHjx/2uGgAAAADgCmJ9glxdXa1vfOMbuu+++1RSUqItW7Zo8uTJ2rZtm99VAwAAAABcQax+SFdnZ6cOHjyodevWuabPmzdPBw4cSLlOR0eHOjo6EuNnz56VJLW1tQ1fRQEAAABY4eLvfmOMzzUZnP87fz6jlz3/3/nzGdyavaxOkM+cOaOenh5FIhHX9EgkopaWlpTrbNq0SY8//njS9MmTJw9LHQEAAADY59y5cwqHw35X45IFg0FFo1GVV5RnfNvRaFTBYDDj27WJ1QnyRY7j/tuJMSZp2kWPPPKIHnroocR4PB7Xv//9bxUWFva5Dgaura1NkydP1okTJ1RQUOB3dSBiYhviYR9iYh9iYh9iYhfiMTjGGJ07d06xWMzvqlyWUCik5uZmdXZ2ZnzbwWBQoVAo49u1idUJ8oQJE5SVlZXUW9za2prUq3xRbm6ucnNzXdPGjRs3XFUctQoKCvjCtgwxsQvxsA8xsQ8xsQ8xsQvxuHwjsef440Kh0KhPVP1i9UO6gsGgysrKVFtb65peW1urOXPm+FQrAAAAAMCVyOoeZEl66KGHtGzZMt12222aPXu2nnrqKR0/flyrVq3yu2oAAAAAgCuI9QlyZWWl3n//fW3YsEGnT5/WzTffrOeff15FRUV+V21Uys3N1WOPPZZ0GTv8Q0zsQjzsQ0zsQ0zsQ0zsQjwA/zhmpD//HAAAAACAIWD1PcgAAAAAAGQKCTIAAAAAACJBBgAAAABAEgkyAAAAAACSSJBHnZqaGhUXFysUCqmsrEyNjY2SpK6uLn3nO9/RjBkzNHbsWMViMX3961/XqVOn0pbZ1NSkiooK5eXl6brrrtOGDRvkffZbfX29ysrKFAqF9IlPfELbt28flv0bafqKh9fKlSvlOI62bNmStkziMTjpYvLGG2/o7rvvVjgcVn5+vmbNmqXjx4/3WyYxGZz+YnL+/Hk98MADmjRpkvLy8lRSUqJt27alLZOYXJ6GhgYtXLhQsVhMjuPo2Wefdc03xqiqqkqxWEx5eXmaO3euXn/99bTlEo/L119MaNv9ke48+Tjad8BCBqPGrl27TE5OjtmxY4c5fPiwWb16tRk7dqx59913zQcffGDuvPNOs3v3bvPPf/7TvPTSS6a8vNyUlZX1W+bZs2dNJBIxS5YsMU1NTWbPnj0mPz/fPPHEE4lljh49asaMGWNWr15tDh8+bHbs2GFycnLMb3/72+HeZav1F4+P27dvn7nllltMLBYzP/7xj/stk3gMTrqYvP3222b8+PFm7dq15pVXXjHvvPOO+d3vfmf+9a9/9VkmMRmcdDG57777zPXXX2/q6upMc3Oz+fnPf26ysrLMs88+22eZxOTyPf/882b9+vVmz549RpLZt2+fa/7mzZtNfn6+2bNnj2lqajKVlZVm4sSJpq2trc8yicfg9BcT2nZ/pDtPLqJ9B+xEgjyKfPaznzWrVq1yTbvxxhvNunXrUi7/17/+1UhKStg+rqamxoTDYdPe3p6YtmnTJhOLxUw8HjfGGPPwww+bG2+80bXeypUrzaxZsy53V64IA4nHyZMnzXXXXWf+8Y9/mKKiorQNKPEYnHQxqaysNF/72tcuqUxiMjjpYlJaWmo2bNjgmj9z5kzz3e9+t88yicnQ8P7wj8fjJhqNms2bNyemtbe3m3A4bLZv395nOcRj6PSXjF1E255ZfcWE9h2wF5dYjxKdnZ06ePCg5s2b55o+b948HThwIOU6Z8+eleM4GjduXGLaihUrNHfu3MT4Sy+9pIqKCteL7OfPn69Tp07p2LFjiWW8250/f77+/ve/q6ura3A7NkINJB7xeFzLli3T2rVrVVpamrIc4jF00sUkHo/r97//vT75yU9q/vz5uvbaa1VeXp506RwxGToDOU9uv/12Pffcc3rvvfdkjFFdXZ3eeustzZ8/P7E8McmM5uZmtbS0uI5bbm6uKioqXO0M8fAXbbv/aN8Bu5EgjxJnzpxRT0+PIpGIa3okElFLS0vS8u3t7Vq3bp3uueceFRQUJKZPnDhRU6ZMSYy3tLSkLPPivP6W6e7u1pkzZwa3YyPUQOLxgx/8QNnZ2frmN7/ZZznEY+iki0lra6vOnz+vzZs3a8GCBdq/f7++/OUva/Hixaqvr08sT0yGzkDOk61bt+qmm27SpEmTFAwGtWDBAtXU1Oj2229PLE9MMuPisUvXzhAP/9C224H2HbBbtt8VQGY5juMaN8YkTevq6tKSJUsUj8dVU1Pjmrdp06YBlemdPpBlRqO+4nHw4EH95Cc/0SuvvNLvMSIeQ6+vmMTjcUnSokWL9OCDD0qSbr31Vh04cEDbt29XRUWFJGIyHPr73tq6datefvllPffccyoqKlJDQ4Puv/9+TZw4UXfeeackYpJp6doZ4uEP2nY70L4D9qMHeZSYMGGCsrKyknqLW1tbXX9t7Orq0le+8hU1NzertrbW9RfmVKLRaMoypf/+ZbOvZbKzs1VYWHjZ+zSSpYtHY2OjWltbNWXKFGVnZys7O1vvvvuuvvWtb2nq1Kl9lks8Ll+6mEyYMEHZ2dm66aabXPNLSkr6fYo1Mbl86WJy4cIFPfroo6qurtbChQv1qU99Sg888IAqKyv1xBNP9FkuMRke0WhUktK2M6nWIx7Di7bdHrTvgP1IkEeJYDCosrIy1dbWuqbX1tZqzpw5kv7bgB45ckR/+MMfBvRlOnv2bDU0NKizszMxbf/+/YrFYokv+tmzZydtd//+/brtttuUk5MzyD0bmdLFY9myZXrttdf06quvJoZYLKa1a9fqhRde6LNc4nH50sUkGAzqM5/5jN58803X/LfeektFRUV9lktMLl+6mHR1damrq0uBgLspy8rKSvT4p0JMhkdxcbGi0ajruHV2dqq+vj7RzqRCPIYXbbtdaN+BESCzzwSDny6+LmXnzp3m8OHDZs2aNWbs2LHm2LFjpqury9x9991m0qRJ5tVXXzWnT59ODB0dHYky1q1bZ5YtW5YY/+CDD0wkEjFLly41TU1NZu/evaagoCDlawcefPBBc/jwYbNz505eO2D6j0cqqZ5ySTyGVrqY7N271+Tk5JinnnrKHDlyxPz0pz81WVlZprGxMVEGMRla6WJSUVFhSktLTV1dnTl69Kh5+umnTSgUMjU1NYkyiMnQOXfunDl06JA5dOiQkWSqq6vNoUOHEk9E3rx5swmHw2bv3r2mqanJLF26NOk1T8RjaPUXE9p2f6Q7T7xo3wG7kCCPMj/72c9MUVGRCQaDZubMmaa+vt4YY0xzc7ORlHKoq6tLrL98+XJTUVHhKvO1114zd9xxh8nNzTXRaNRUVVUlXjlw0Ysvvmg+/elPm2AwaKZOnWq2bds23Ls6IvQVj1RSNaDEY+ili8nOnTvNDTfcYEKhkLnllluS3rdLTIZefzE5ffq0WbFihYnFYiYUCpnp06ebH/3oR67jS0yGTl1dXcp2Yvny5caY3lc9PfbYYyYajZrc3Fzzuc99zjQ1NbnKIB5Dq7+Y0Lb7I9154kX7DtjFMeaju/cBAAAAABjFuAcZAAAAAACRIAMAAAAAIIkEGQAAAAAASSTIAAAAAABIIkEGAAAAAEASCTIAAAAAAJJIkAEAAAAAkESCDAAAAACAJBJkAMAIVlVVpVtvvdXvagAAgCuEY4wxflcCAAAvx3H6nb98+XI9+eST6ujoUGFhYYZqBQAArmQkyAAAK7W0tCT+v3v3bn3ve9/Tm2++mZiWl5encDjsR9UAAMAVikusAQBWikajiSEcDstxnKRp3kusV6xYoS996UvauHGjIpGIxo0bp8cff1zd3d1au3atxo8fr0mTJumXv/yla1vvvfeeKisrdfXVV6uwsFCLFi3SsWPHMrvDAADAdyTIAIAryh//+EedOnVKDQ0Nqq6uVlVVlb74xS/q6quv1l/+8hetWrVKq1at0okTJyRJH374oT7/+c/rqquuUkNDg/70pz/pqquu0oIFC9TZ2enz3gAAgEwiQQYAXFHGjx+vrVu3avr06br33ns1ffp0ffjhh3r00Uc1bdo0PfLIIwoGg/rzn/8sSdq1a5cCgYB+8YtfaMaMGSopKdHTTz+t48eP68UXX/R3ZwAAQEZl+10BAACGUmlpqQKB//79NxKJ6Oabb06MZ2VlqbCwUK2trZKkgwcP6u2331Z+fr6rnPb2dr3zzjuZqTQAALACCTIA4IqSk5PjGnccJ+W0eDwuSYrH4yorK9Ovf/3rpLKuueaa4asoAACwDgkyAGBUmzlzpnbv3q1rr71WBQUFflcHAAD4iHuQAQCj2le/+lVNmDBBixYtUmNjo5qbm1VfX6/Vq1fr5MmTflcPAABkEAkyAGBUGzNmjBoaGjRlyhQtXrxYJSUluvfee3XhwgV6lAEAGGUcY4zxuxIAAAAAAPiNHmQAAAAAAESCDAAAAACAJBJkAAAAAAAkkSADAAAAACCJBBkAAAAAAEkkyAAAAAAASCJBBgAAAABAEgkyAAAAAACSSJABAAAAAJBEggwAAAAAgCQSZAAAAAAAJEn/D1LFFnTdseUvAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "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": 16, - "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": "357206ab7e4935423e95e994af80e27e7e6c0672abcebb9d86ab743298213348" - }, - "kernelspec": { - "display_name": "Python 3.9.7 ('base')", - "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": "3.9.15" - } - }, - "nbformat": 4, - "nbformat_minor": 4 + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyzing ADCP Data with MHKiT\n", + "\n", + "The following example illustrates a straightforward workflow for analyzing Acoustic Doppler Current Profiler (ADCP) data utilizing MHKiT. MHKiT has integrated the DOLfYN codebase as a module to facilitate ADCP and Acoustic Doppler Velocimetry (ADV) data processing.\n", + "\n", + "Here is a standard workflow for ADCP data analysis:\n", + "\n", + "1. **Import Data**\n", + "\n", + "2. **Review, QC, and Prepare the Raw Data**:\n", + " 1. Calculate or verify the correctness of depth bin locations\n", + " 2. Discard data recorded above the water surface or below the seafloor\n", + " 3. Assess the quality of velocity, beam amplitude, and/or beam correlation data\n", + " 4. Rotate Data Coordinate System\n", + "\n", + "3. **Data Averaging**: \n", + " - If not already executed within the instrument, average the data into time bins of a predetermined duration, typically between 5 and 10 minutes\n", + "\n", + "4. **Speed and Direction**\n", + "\n", + "5. **Plotting**\n", + "\n", + "6. **Saving and Loading DOLfYN datasets**\n", + "\n", + "7. **Turbulence Statistics**\n", + " 1. Turbulence Intensity (TI)\n", + " 2. Power Spectral Densities\n", + " 3. Instrument Noise\n", + " 4. TKE Dissipation Rate\n", + " 5. Noise-corrected TI\n", + " 6. TKE Componenets\n", + " 7. TKE Production\n", + " 8. TKE Balance \n", + "\n", + "\n", + "Begin your analysis by importing the requisite tools:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\mcve343\\anaconda3\\envs\\work\\lib\\site-packages\\xarray\\backends\\cfgrib_.py:29: UserWarning: Failed to load cfgrib - most likely there is a problem accessing the ecCodes library. Try `import cfgrib` to get the full error message\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import dolfyn\n", + "from dolfyn.adp import api" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Importing Raw Instrument Data\n", + "\n", + "One of DOLfYN's key features is its ability to directly import raw data from an Acoustic Doppler Current Profiler (ADCP) right after it has been transferred. In this instance, we are using a Nortek Signature1000 ADCP, with the data stored in files with an '.ad2cp' extension. This specific dataset represents several hours of velocity data, captured at 1 Hz by an ADCP mounted on a bottom lander within a tidal inlet. The list of instruments compatible with DOLfYN can be found in the [MHKiT DOLfYN documentation](https://mhkit-software.github.io/MHKiT/mhkit-python/api.dolfyn.html).\n", + "\n", + "We'll start by importing the raw data file downloaded from the instrument. The `read` function processes the raw file and converts the information into an xarray Dataset. This Dataset includes several groups of variables:\n", + "\n", + "1. **Velocity**: Recorded in the coordinate system saved by the instrument (beam, XYZ, ENU)\n", + "2. **Beam Data**: Includes amplitude and correlation data\n", + "3. **Instrumental & Environmental Measurements**: Captures the instrument's bearing and environmental conditions\n", + "4. **Orientation Matrices**: Used by DOLfYN for rotating through different coordinate frames.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading file ../dolfyn/example_data/Sig1000_tidal.ad2cp ...\n" + ] + } + ], + "source": [ + "ds = dolfyn.read(\"../dolfyn/example_data/Sig1000_tidal.ad2cp\")" + ] + }, + { + "attachments": {}, + "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": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:              (time: 55000, time_b5: 55000, range: 28, range_b5: 28,\n",
+       "                          beam: 4, dir: 4, earth: 3, inst: 3, dirIMU: 3, q: 4,\n",
+       "                          x1: 4, x2: 4)\n",
+       "Coordinates:\n",
+       "  * time                 (time) datetime64[ns] 2020-08-15T00:20:00.500999927 ...\n",
+       "  * time_b5              (time_b5) datetime64[ns] 2020-08-15T00:20:00.4384999...\n",
+       "  * range                (range) float64 0.6 1.1 1.6 2.1 ... 12.6 13.1 13.6 14.1\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",
+       "  * dir                  (dir) <U2 'E' 'N' 'U1' 'U2'\n",
+       "  * earth                (earth) <U1 'E' 'N' 'U'\n",
+       "  * inst                 (inst) <U1 'X' 'Y' 'Z'\n",
+       "  * dirIMU               (dirIMU) <U1 'E' 'N' 'U'\n",
+       "  * q                    (q) <U1 'w' 'x' 'y' 'z'\n",
+       "  * x1                   (x1) int32 1 2 3 4\n",
+       "  * x2                   (x2) 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  (x1, x2) float32 1.183 0.0 -1.183 ... 0.5518 0.0 0.5518\n",
+       "Attributes: (12/34)\n",
+       "    filehead_config:       {"CLOCKSTR": {"TIME": "\\"2020-08-13 13:56:21\\""}, ...\n",
+       "    inst_model:            Signature1000\n",
+       "    inst_make:             Nortek\n",
+       "    inst_type:             ADCP\n",
+       "    burst_config:          {"press_valid": true, "temp_valid": true, "compass...\n",
+       "    n_cells:               28\n",
+       "    ...                    ...\n",
+       "    proc_idle_less_12pct:  0\n",
+       "    rotate_vars:           ['vel', 'accel', 'accel_b5', 'angrt', 'angrt_b5', ...\n",
+       "    coord_sys:             earth\n",
+       "    fs:                    1\n",
+       "    has_imu:               1\n",
+       "    beam_angle:            25
" + ], + "text/plain": [ + "\n", + "Dimensions: (time: 55000, time_b5: 55000, range: 28, range_b5: 28,\n", + " beam: 4, dir: 4, earth: 3, inst: 3, dirIMU: 3, q: 4,\n", + " x1: 4, x2: 4)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2020-08-15T00:20:00.500999927 ...\n", + " * time_b5 (time_b5) datetime64[ns] 2020-08-15T00:20:00.4384999...\n", + " * range (range) float64 0.6 1.1 1.6 2.1 ... 12.6 13.1 13.6 14.1\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", + " * dir (dir) : 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" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Initial Steps for Data Quality Control (QC)\n", + "\n", + "### 2.1: Set the Deployment Height\n", + "\n", + "When using Nortek instruments, the deployment software does not factor in the deployment height. The deployment height represents the position of the Acoustic Doppler Current Profiler (ADCP) within the water column. \n", + "\n", + "In this context, the center of the first depth bin is situated at a distance that is the sum of three elements: \n", + "1. Deployment height (the ADCP's position in the water column)\n", + "2. Blanking distance (the minimum distance from the ADCP to the first measurement point)\n", + "3. Cell size (the vertical distance of each measurement bin in the water column)\n", + "\n", + "To ensure accurate readings, it is critical to calibrate the 'range' coordinate to make '0' correspond to the seafloor. This calibration can be achieved using the `set_range_offset` function. This function is also useful when working with a down-facing instrument as it helps account for the depth below the water surface. \n", + "\n", + "For those using a Teledyne RDI ADCP, the TRDI deployment software will prompt you to specify the deployment height/depth during setup. If there's a need for calibration post-deployment, the `set_range_offset` function can be utilized in the same way as described above." + ] + }, + { + "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": {}, + "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)" + ] + }, + { + "attachments": {}, + "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": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<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" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2. Discard Data Above Surface Level\n", + "\n", + "To reduce computational load, we can exclude all data at or above the water surface level. Since the instrument was oriented upwards, we can utilize the pressure sensor data along with the function `find_surface_from_P`. However, this approach necessitates that the pressure sensor was calibrated or 'zeroed' prior to deployment. If the instrument is facing downwards or doesn't include pressure data, the function `find_surface` can be used to detect the seabed or water surface.\n", + "\n", + "It's important to note that Acoustic Doppler Current Profilers (ADCPs) do not measure water salinity, so you'll need to supply this information to the function. The dataset returned by this function includes an additional variable, \"depth\". If `find_surface_from_P` is invoked after `set_range_offset`, \"depth\" represents the distance from the water surface to the seafloor. Otherwise, it indicates the distance to the ADCP pressure sensor.\n", + "\n", + "After determining the \"depth\", you can use the nan_beyond_surface function to discard data in depth bins at or above the actual water surface. Be aware that this function will generate 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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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ds[\"vel\"][1].plot()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3: Apply an Acoustic Signal Correlation Filter\n", + "\n", + "After removing data from bins at or above the water surface, we typically apply a filter based on acoustic signal correlation to the ADCP data. This helps to eliminate erroneous velocity data points, which can be caused by factors such as bubbles, kelp, fish, etc., moving through one or multiple beams.\n", + "\n", + "You can quickly inspect the data to determine an appropriate correlation value by using the built-in plotting feature of xarray. In the following example, we use xarray's slicing capabilities to display data from beam 1 within a range of 0 to 10 m from the ADCP.\n", + "\n", + "It's important to note that not all ADCPs provide acoustic signal correlation data, which serves as a quantitative measure of signal quality. Older ADCPs may not offer this feature, in which case you can skip this step when using such 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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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "ds[\"corr\"].sel(beam=1, range=slice(0, 10)).plot()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's beneficial to also review data from the other beams. A significant portion of this data is of high quality. To avoid discarding valuable data with lower correlations, which could be due to natural variations, we can use the `correlation_filter`. This function assigns a value of NaN (not a number) to velocity values corresponding to correlations below 50%.\n", + "\n", + "However, it's important to note that the correlation threshold is dependent on the specifics of the deployment environment and the instrument used. It's not unusual to set a threshold as low as 30%, or even to forgo the use of this function entirely." + ] + }, + { + "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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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ds[\"vel\"][1].plot()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Rotate Data Coordinate System\n", + "\n", + "After cleaning the data, the next step is to rotate the velocity data into accurate East, North, Up (ENU) coordinates.\n", + "\n", + "ADCPs utilize an internal compass or magnetometer to determine magnetic ENU directions. You can use the set_declination function to adjust the velocity data according to the magnetic declination specific to your geographical coordinates. This declination can be looked up online for specific coordinates.\n", + "\n", + "Instruments save vector data in the coordinate system defined in the deployment configuration file. To make this data meaningful, it must be transformed through various coordinate systems (\"beam\"<->\"inst\"<->\"earth\"<->\"principal\"). This transformation is accomplished using the `rotate2` function. If the \"earth\" (ENU) coordinate system is specified, DOLfYN will automatically rotate the dataset through the required coordinate systems to reach the \"earth\" coordinates. Setting `inplace` to true will modify the input dataset directly, meaning it will not create a new dataset.\n", + "\n", + "In this case, since the ADCP data is already in the \"earth\" coordinate system, the `rotate2` function will return the input dataset without modifications. The `set_declination` function will work 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)" + ] + }, + { + "attachments": {}, + "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)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Average the Data\n", + "\n", + "As this deployment was configured in \"burst mode\", a standard step in the analysis process is to average the velocity data into time bins. \n", + "\n", + "However, if the instrument was set up in an \"averaging mode\" (where a specific profile and/or average interval was set, for instance, averaging 5 minutes of data every 30 minutes), this step would have been performed within the ADCP during deployment and can thus be skipped.\n", + "\n", + "To average the data into time bins (also known as ensembles), you should first initialize the binning tool `ADPBinner`. The parameter \"n_bin\" represents the number of data points in each ensemble. In this case, we're dealing with 300 seconds' worth of data. The \"fs\" parameter stands for the sampling frequency, which for this deployment is 1 Hz. Once the binning tool is initialized, you can use the `do_avg` function to average the data into ensembles." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "avg_tool = api.ADPBinner(n_bin=ds.fs * 300, fs=ds.fs)\n", + "ds_avg = avg_tool.do_avg(ds)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:         (time: 183, dirIMU: 3, range: 28, dir: 4, beam: 4,\n",
+       "                     earth: 3, inst: 3, q: 4, time_b5: 183, range_b5: 28)\n",
+       "Coordinates:\n",
+       "  * time            (time) datetime64[ns] 2020-08-15T00:22:30.001030683 ... 2...\n",
+       "  * dirIMU          (dirIMU) <U1 'E' 'N' 'U'\n",
+       "  * range           (range) float64 1.2 1.7 2.2 2.7 3.2 ... 13.2 13.7 14.2 14.7\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] 2020-08-15T00:22:29.938495159 .....\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) float32 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) float32 72.37 72.4 72.38 ... -197.1 -197.1\n",
+       "    accel           (dirIMU, time) float32 -0.3584 -0.361 ... 9.714 9.712\n",
+       "    ...              ...\n",
+       "    boost_running   (time) float32 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 10.23 ... 10.14 10.15 10.15\n",
+       "Attributes: (12/41)\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",
+       "    has_imu:                   1\n",
+       "    beam_angle:                25\n",
+       "    h_deploy:                  0.6\n",
+       "    declination:               15.8\n",
+       "    declination_in_orientmat:  1\n",
+       "    principal_heading:         11.1898
" + ], + "text/plain": [ + "\n", + "Dimensions: (time: 183, dirIMU: 3, range: 28, dir: 4, beam: 4,\n", + " earth: 3, inst: 3, q: 4, time_b5: 183, range_b5: 28)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2020-08-15T00:22:30.001030683 ... 2...\n", + " * dirIMU (dirIMU) " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "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=(10, 6)).add_axes([0.14, 0.14, 0.8, 0.74])\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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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.figure(figsize=(10, 6)).add_axes([0.14, 0.14, 0.8, 0.74])\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]\");" + ] + }, + { + "attachments": {}, + "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')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Turbulence Statistics\n", + "\n", + "The next section of this jupyter notebook will run through the turbulence analysis of the data presented here. There was no intention of measuring turbulence in the deployment that collected this data, so results depicted here are not the highest quality. The quality of turbulence measurements from an ADCP depend heavily on the quality of the deployment setup and data collection, particularly instrument frequency, samping frequency and depth bin size.\n", + "\n", + "Read more on proper ADCP setup for turbulence measurements in: Thomson, Jim, et al. \"Measurements of turbulence at two tidal energy sites in Puget Sound, WA.\" IEEE Journal of Oceanic Engineering 37.3 (2012): 363-374.\n", + "\n", + "Most functions related to turbulence statistics in MHKiT-DOLfYN have the papers they originate from referenced in their docstrings.\n", + "\n", + "### 7.1 Turbulence Intensity\n", + "For most users, turbulence intensity (TI), the ratio of the ensemble standard deviation to ensemble flow speed given as a percent, is all most will need. In MHKiT, this can be simply calculated as `.velds.I`, but be aware that this will be a conservative estimate. Another function, `calc_ti`, is capable of subtracting instrument noise from this parameter and is discussed below. The noise-subtracted TI is more accurate and typically 1-2% lower than the non-noise-subtracted estimation.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Turbulence Intensity\n", + "ds_avg[\"TI\"] = ds_avg.velds.I\n", + "ds_avg[\"TI\"].plot(cmap=\"Reds\", ylim=(0, 11))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.2 Power Spectral Densities (Auto-Spectra)\n", + "\n", + "Other turbulence parameters include the TKE power- and cross-spectral densities (i.e the power spectra), turbulent kinetic energy (TKE, i.e. the variances of velocity vector components), Reynolds stress vector (i.e. the co-variances of velocity vector components), TKE dissipation rate, and TKE production rate. These quantities are primarily used to inform and verify hydrodynamic and coastal models, which take some or all of these quantities as input.\n", + "\n", + "The TKE production rate is the rate at which kinetic energy (KE) transitions from a useful state (able to do \"work\" in the physics sense) to turbulent; TKE is the actual amount of turbulent KE in the water; and TKE dissipation rate is the rate at which turbulent KE is lost to non-motion forms of energy (heat, sound, etc) due to viscosity. The power spectra are used to depict and quantify this energy in the frequency domain, and creating them are the first step in turbulence analysis.\n", + "\n", + "We'll start by looking at the power spectra, specifically the auto-spectra from the vertical beam (\"auto\" meaning the variance of a single vector direction, e.g. $\\overline{u'^2}$, vs \"cross\", meaning the covariance of two directions, e.g. $\\overline{u'w'}$). This can be done using the `calc_psd` function from the `ADPBinner` we created (\"avg_tool\"). We'll create spectra at the middle water column, at a depth of 5 m, and use a number of FFT's equal to 1/3 the bin size." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "rng = 5 # m\n", + "vel_up = ds[\"vel_b5\"].sel(range_b5=rng, method=\"nearest\") # vertical velocity\n", + "U = ds_avg[\"U_mag\"].sel(\n", + " range=5, method=\"nearest\"\n", + ") # flow speed, for plotting in the next block\n", + "\n", + "ds_avg[\"auto_spectra_5m\"] = avg_tool.calc_psd(\n", + " vel_up, freq_units=\"Hz\", n_fft=ds_avg.n_bin // 3\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the auto-spectra, we're primarly looking for three components: the energy-producing region, the isotropic turbulence region (so-called \"red noise\"), and the instrument noise floor (termed \"white noise\"). \n", + "\n", + "The block below organizes and plots the power spectra by the corresponding ensemble speed, averaging them by 0.1 m/s velocity bins. Note that if an ensemble is missing data that wasn't filled in, a power spectrum will not be calculated for that ensemble timestamp." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(0.5, 0, 'Frequency [Hz]'),\n", + " Text(0, 0.5, 'PSD [m2 s-2 Hz-1]'),\n", + " (0.01, 1),\n", + " (0.0005, 0.1)]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "\n", + "plt.rcParams.update({\"font.size\": 18, \"font.family\": \"Times New Roman\"})\n", + "\n", + "\n", + "def plot_spectra_by_color(auto_spectra, U_mag, ax, fig, cbar_max=4.0):\n", + " U = U_mag.values\n", + " U_max = U_mag.max().values\n", + "\n", + " # Average spectra into 0.1 m/s velocity bins\n", + " speed_bins = np.arange(0.5, U_max, 0.1)\n", + " time = [t for t in auto_spectra.dims if \"time\" in t][0]\n", + " S_group = auto_spectra.assign_coords({time: U}).rename({time: \"speed\"})\n", + " group = S_group.groupby_bins(\"speed\", speed_bins)\n", + " count = group.count().values\n", + " S = group.mean()\n", + "\n", + " # define the colormap\n", + " cmap = plt.cm.turbo\n", + " # define the bins and normalize\n", + " bounds = np.arange(0.5, cbar_max, 0.1)\n", + " norm = mpl.colors.BoundaryNorm(bounds, cmap.N)\n", + " colors = cmap(norm(speed_bins))\n", + "\n", + " # plot\n", + " for i in range(len(speed_bins) - 1):\n", + " ax.loglog(auto_spectra[\"freq\"], S[i], c=colors[i])\n", + " ax.grid()\n", + "\n", + " # create a second axes for the colorbar\n", + " cax = fig.add_axes([0.8, 0.07, 0.03, 0.88])\n", + " # cax, _ = mpl.colorbar.make_axes(fig.gca())\n", + " sm = mpl.colorbar.ColorbarBase(\n", + " cax,\n", + " cmap=cmap,\n", + " norm=norm,\n", + " spacing=\"proportional\",\n", + " ticks=bounds,\n", + " boundaries=bounds,\n", + " format=\"%1.1f\",\n", + " label=\"Velocity [m/s]\",\n", + " )\n", + "\n", + " # Add -5/3 slope line\n", + " m = -5 / 3\n", + " x = np.logspace(-1, 0.5)\n", + " y = 10 ** (-3) * x**m\n", + " ax.loglog(x, y, \"--\", c=\"black\", label=\"$f^{-5/3}$\")\n", + " ax.legend()\n", + "\n", + " return ax, sm\n", + "\n", + "\n", + "# Set up figure\n", + "fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n", + "fig.subplots_adjust(left=0.2, right=0.75, top=0.95, bottom=0.1)\n", + "\n", + "# Plot spectra by color\n", + "plot_spectra_by_color(ds_avg[\"auto_spectra_5m\"], U, ax, fig, cbar_max=2.0)\n", + "# Set axes\n", + "ax.set(\n", + " xlabel=\"Frequency [Hz]\",\n", + " ylabel=\"PSD [m2 s-2 Hz-1]\",\n", + " xlim=(0.01, 1),\n", + " ylim=(0.0005, 0.1),\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the figure above, we can see the energy-producing turbulent structures below a frequency of 0.2 Hz (one tick to the right of \"10^-1\"). The isotropic turbulence cascade, seen by the dashed f^(-5/3) slope (from Kolmogorov's theory of turbulence) begins at around 0.2 Hz and continues until we reach the Nyquist frequency at 0.5 Hz (1/2 the instrument's sampling frequency, 1 Hz). The instrument's noise floor can't be seen here, but will show up as the flattened part of the spectra at the highest frequencies. For this instrument (Nortek Signature1000), the noise floor typically varies around 10^-3, depending on flow speed and range distance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.3 Instrument Noise\n", + "\n", + "The next thing we want to do is calculate the instrument's Doppler noise floor from the spectrum we calculated above. (We are making the assumption that the noise floor of the vertical beam is the same as the noise floor of the other 4 beams). This gives us a timeseries of the noise floor, which varies by instrument and with flow speed, at that depth bin.\n", + "\n", + "We can do this using the `calc_doppler_noise` function. The two inputs for this function are the power spectra and \"pct_fN\", the percent of the Nyquist frequency that the noise floor exists. Because in this particularly dataset we can't see the noise floor, we'll just use 90% or pct_fN=0.9 as an example. If the noise floor began at 0.4 Hz and ran til our maximum frequency of 0.5 Hz, we'd use pct_fN = 0.4 Hz / 0.5 Hz = 0.8." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "ds_avg[\"noise_5m\"] = avg_tool.calc_doppler_noise(ds_avg[\"auto_spectra_5m\"], pct_fN=0.9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.4 TKE Dissipation Rate\n", + "\n", + "Because we can see the isotropic turbulence cascade (0.2 - 0.5 Hz) at this depth bin (5 m altitude), we can calculate the TKE dissipation rate at this location from the spectra itself. This can be done using `calc_dissipation_LT83`, whose inputs are the power spectra, the ensemble speed, the frequency range of the isotropic cascade, and the instrument's noise." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Frequency range of isotropic turubulence cascade in same units as PSD frequency vector\n", + "f_rng = [0.2, 0.5]\n", + "# Dissipation rate\n", + "ds_avg[\"dissipation_rate_5m\"] = avg_tool.calc_dissipation_LT83(\n", + " ds_avg[\"auto_spectra_5m\"], U, freq_range=f_rng, noise=ds_avg['noise_5m']\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have just found the spectra and dissipation rate from a single depth bin at an altitude of 5 m from the seafloor, but typically we want the spectra and dissipation rates from the entire measurement profile. If we want to look at the spectra and dissipation rates from all depth bins, we can set up a \"for\" loop on the range coordinate and merge them together:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "\n", + "spec = [None] * len(ds.range)\n", + "e = [None] * len(ds.range)\n", + "n = [None] * len(ds.range)\n", + "\n", + "for r in range(len(ds[\"range\"])):\n", + " # Calc spectra from each depth bin using the 5th beam\n", + " spec[r] = avg_tool.calc_psd(\n", + " ds[\"vel_b5\"].isel(range_b5=r), freq_units=\"Hz\"\n", + " )\n", + " \n", + " # Calculate doppler noise from spectra from each depth bin\n", + " n[r] = avg_tool.calc_doppler_noise(spec[r], pct_fN=0.9)\n", + " \n", + " # Calc dissipation rate from each spectra\n", + " e[r] = avg_tool.calc_dissipation_LT83(\n", + " spec[r], ds_avg.velds.U_mag.isel(range=r), freq_range=f_rng, noise=n[r]\n", + " )\n", + "\n", + "ds_avg[\"auto_spectra\"] = xr.concat(spec, dim=\"range\")\n", + "ds_avg[\"noise\"] = xr.concat(n, dim=\"range\")\n", + "ds_avg[\"dissipation_rate\"] = xr.concat(e, dim=\"range\")\n", + "\n", + "del spec, n, e # save memory" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a profile timeseries of dissipation rate, we need apply some quality control (QC). Since we can't look at each individual spectrum to ensure we can see the isotropic turbulence cascade, we want to QC the output from `calc_dissipation_LT83` to make sure what was calculated actually falls on a f^(-5/3) slope. We can do this using the function `check_turbulence_cascade_slope`, which uses linear regression on the log-transformed LT83 equation (ref. to Lumley and Terray, 1983, see docstring) to calculate the spectral slope for the given frequency range. \n", + "\n", + "In our case, we're calculating the slope of each spectrum between 0.2 and 0.5 Hz. We'll use a cutoff of 20% for the error, but this can be lowered if there still appear to be erroneous estimations from visual inspection of the spectra." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Quality control dissipation rate estimation\n", + "slope = avg_tool.check_turbulence_cascade_slope(\n", + " ds_avg[\"auto_spectra\"], freq_range=f_rng\n", + ")\n", + "\n", + "# Check that percent difference from -5/3 is not greater than 20%\n", + "mask = abs((slope[0].values - (-5 / 3)) / (-5.3)) <= 0.20\n", + "\n", + "# Keep good data\n", + "ds_avg[\"dissipation_rate\"] = ds_avg[\"dissipation_rate\"].where(mask)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we plot the dissipation rate below in a colormap, we can see that the profile map has a lot of missing data. One of the reasons is that the 1 Hz sampling rate doesn't provide enough information needed to make dissipation rate estimations, and the other part is that turbulence measurements push the boundaries of what ADCPs are capable of.\n", + "\n", + "Also, 1x10^-4 to 3x10^-4 $m^2/s^3$ is reasonable for a dissipation rate estimate for the 1 - 1.5 m/s current speeds measured here. They can be a magnitude greater for faster flow speeds, typically increase closer to the seafloor, and depend heavily on bathymetry and regional hydrodynamics." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ds_avg[\"dissipation_rate\"].plot(cmap=\"turbo\", ylim=(0, 11))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.5 Noise-Corrected Turbulence Intensity\n", + "\n", + "Now that we've calculated the noise floor for each ping, we can recalculate TI and include subtracting instrument noise using the `calc_ti` function. If we subtract this from the non-noise corrected function, we can see there's a large difference\n", + "at slower slow speeds, but the average difference is about 0.008 (0.8%). Notice this will also remove measurements where noise is \n", + "high." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'TI Difference')" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ds_avg[\"turbulence_intensity\"] = avg_tool.calc_ti(ds.velds.U_mag, noise=ds_avg[\"noise\"])\n", + "\n", + "(ds_avg[\"TI\"] - ds_avg[\"turbulence_intensity\"]).plot(cmap=\"Greens\", ylim=(0, 11))\n", + "plt.title(\"TI Difference\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.6 Reynolds Stress Components\n", + "\n", + "The next parameters we'll find here are the Reynolds normal and shear stresses (-$\\overline{u_iu_j}$). Since we're using the vertical beam on the ADCP, we can directly measure the vertical TKE component from the along-beam velocity using the `calc_tke` function. This function is capable of calculating TKE for any along-beam velocity.\n", + "\n", + "We can also use the so-called \"beam-variance\" equations to estimate the Reynolds stress tensor components (i.e. $\\overline{u'^2}$, $\\overline{v'^2}$, $\\overline{w'^2}$, $\\overline{u'v'}$, $\\overline{u'w'^2}$, $\\overline{v'w'^2}$), which define the normal and shear stresses acting on an element of water. These equations are built into the functions `calc_stress_5beam` and `calc_stress_4beam`. \n", + "\n", + "Both of these functions will give comparable results, but `calc_stress_5beam` takes into account instrument tilt, and `calc_stress_4beam` does not. Both will throw a tilt warning if tilt is greater than 5 degrees.\n", + "\n", + "#### Quick 5-beam ADCP lesson before we dive in:\n", + "\n", + "There are a couple caveats to calculating Reynolds stress tensor components:\n", + " 1. Because this instrument only has 5 beams, we can only find 5 of the 6 components (6 unkowns, 5 knowns)\n", + " 2. Because the ADCP's instrument (XYZ) axes weren't aligned with the flow during deployment, we don't know what direction these components are aligned to (i.e. the 'u' direction is not necessarily the streamwise direction)\n", + " 3. It is possible to rotate the tensor, but we'd need to know all 6 components to do so properly (\"coupled ADCPs\")\n", + " 4. Measurements close to the seafloor can be suspect due to increased vertical flow. ADCPs operate under the \"assumption of homogeneity\", which means that they can only accurate measure consistent horizontal currents with relatively little vertical motion." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Vertical TKE component (w'w' bar)\n", + "ds_avg[\"wpwp_bar\"] = avg_tool.calc_tke(ds[\"vel_b5\"], noise=ds_avg[\"noise\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, as an example, we'll calculate the Reynolds stresses from the `calc_stress_5beam` function, which calculates the individual Reynolds stress tensor components and takes the same inputs: the raw dataset in \"beam\" coordinates, the instrument Doppler noise, the ADCP's orientation and its beam angle. It outputs both the normal stress (\"tke_vec\") and the shear stress (\"stress_vec\") vector. Note, this function will drop at least one warning every time it's run, primarily the coordinate system warning. This function also requires the input raw data to be in beam coordinates, so we'll create a copy of the raw data and rotate it to 'beam'. If you do not, this function will do so automatically." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mcve343\\dolfyn\\dolfyn\\adp\\turbulence.py:260: UserWarning: The beam-variance algorithms assume the instrument's (XYZ) coordinate system is aligned with the principal flow directions.\n", + " warnings.warn(\" The beam-variance algorithms assume the instrument's \"\n" + ] + } + ], + "source": [ + "ds_beam = dolfyn.rotate2(ds, \"beam\", inplace=False)\n", + "ds_avg[\"tke_vec\"], ds_avg[\"stress_vec\"] = avg_tool.stress_tensor_5beam(\n", + " ds_beam, noise=ds_avg[\"noise\"], orientation=\"up\", beam_angle=25\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is one other important thing to note on Reynolds stress measurements by ADCPs: the minimum turbulence length scale that the ADCP is capable of measuring increases with range from the instrument. This means the instrument is only capable of measuring the stress transported by larger and larger turbulent structures as the beams travel farther and farther from the instrument head. One of the benefits of calculating w'w' from the vertical beam is that it isn't limited by this beam spread issue, though on its own it may not be particularly useful." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.7 TKE Production\n", + "\n", + "Though it can't be found from this deployment, we'll go over how to estimate the TKE production rate. There isn't a specific function in MHKiT-DOLfYN for production, but all the necessary variables are. \n", + "\n", + "It is possible to estimate production rates from either an ADV or an ADCP aligned with the flow direction (so \"X\" would align with the principal flow direction). The following estimation for production rates takes into account both along- and cross-stream shear:\n", + "\n", + "$P = -\\overline{u'w'}\\frac{du}{dz} - \\overline{v'w'}\\frac{dv}{dz})$\n", + "\n", + "We found the Reynolds shear stresses -$\\overline{u'w'}$ and -$\\overline{v'w'}$ above using the Reynolds stress equations. If ADV data is available, those estimations are preferred because the ADV's point measurement does not have the assumptions that ADCP measurements have.\n", + "\n", + "The velocity shear components can be found from the aptly named functions `dudz` and `dvdz` in ADPBinner. These functions, which are useful alone in their own right, approximate the shear in the velocity vector between respective depth bins. There is always correlation between velocity measurements in adjacent depth bins, based on ADCP operation principles, which is why \"approximate\" is also used here for velocity shear.\n", + "\n", + "The velocity shear functions operate on the raw velocity vector in the principal reference frame and need to be ensemble-averaged here. This can be done by nesting the `d*dz` function within the ADPBinner's `mean` function. With the ensemble shear known, we can put all the components together to get a production estimation. If using ADV data, take the mean again of the \"range\" dimension." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mcve343\\dolfyn\\dolfyn\\adp\\turbulence.py:260: UserWarning: The beam-variance algorithms assume the instrument's (XYZ) coordinate system is aligned with the principal flow directions.\n", + " warnings.warn(\" The beam-variance algorithms assume the instrument's \"\n", + "C:\\Users\\mcve343\\dolfyn\\dolfyn\\adp\\turbulence.py:268: UserWarning: 100.0 % of measurements have a tilt greater than 5 degrees.\n", + " warnings.warn(f\" {pct_above_thresh} % of measurements have a tilt \"\n" + ] + } + ], + "source": [ + "# Vertical shear gradients\n", + "upwp_ = ds_avg[\"tke_vec\"][1]\n", + "vpwp_ = ds_avg[\"tke_vec\"][2]\n", + "\n", + "# Find and ensemble-average shear\n", + "dudz = avg_tool.mean(avg_tool.dudz(ds_streamwise[\"vel\"]).values)\n", + "dvdz = avg_tool.mean(avg_tool.dvdz(ds_streamwise[\"vel\"]).values)\n", + "\n", + "# Calculate Production\n", + "P = -(upwp_ * dudz + vpwp_ * dvdz)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7.8 TKE Balance \n", + "If we plot the production rates, we can see that they are sensitive to direction. We can also compare the magnitude of production rates to the dissipation rates to get an understanding of the TKE balance. In a well mixed coastal environment, we expect production and dissipation to be approximately equal. Our production estimates aren't accurate because our stress components aren't aligned with the flow, so if we plot them, we see drastic differences (4x10^-3 $m^2/s^3$ is quite large) profile here." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot production rate\n", + "P.plot(cmap=\"coolwarm\", ylim=(0, 11))\n", + "plt.title(\"TKE Production\") # remove bogus title\n", + "\n", + "\n", + "# Plot difference between production and dissipation rate\n", + "plt.figure()\n", + "balance = abs(P) - ds_avg[\"dissipation_rate\"].values\n", + "balance.plot(ylim=(0, 11))\n", + "plt.title(\"TKE Balance\")" + ] + } + ], + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/docs/ADV_Example.ipynb b/docs/ADV_Example.ipynb index 237ca171..3965a23a 100644 --- a/docs/ADV_Example.ipynb +++ b/docs/ADV_Example.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -59,19 +59,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading file ../dolfyn/example_data/vector_data01.VEC ...\n" - ] - } - ], + "outputs": [], "source": [ "ds = dolfyn.read('../dolfyn/example_data/vector_data01.VEC')" ] @@ -85,488 +77,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-                                          "  * inst                 (inst) <U1 'X' 'Y' 'Z'\n",
-                                          "Data variables: (12/15)\n",
-                                          "    beam2inst_orientmat  (x, x*) float64 2.709 -1.34 -1.364 ... -0.3438 -0.3499\n",
-                                          "    batt                 (time) float32 13.2 13.2 13.2 13.2 ... nan nan nan nan\n",
-                                          "    c_sound              (time) float32 1.493e+03 1.493e+03 ... nan nan\n",
-                                          "    heading              (time) float32 5.6 10.5 10.51 10.52 ... nan nan nan nan\n",
-                                          "    pitch                (time) float32 -31.5 -31.7 -31.69 ... nan nan nan\n",
-                                          "    roll                 (time) float32 0.4 4.2 4.253 4.306 ... nan nan nan nan\n",
-                                          "    ...                   ...\n",
-                                          "    orientation_down     (time) bool True True True True ... True True True True\n",
-                                          "    vel                  (dir, time) float32 -1.002 -1.008 -0.944 ... nan nan\n",
-                                          "    amp                  (beam, time) uint8 104 110 111 113 108 ... 0 0 0 0 0\n",
-                                          "    corr                 (beam, time) uint8 97 91 97 98 90 95 95 ... 0 0 0 0 0 0\n",
-                                          "    pressure             (time) float64 5.448 5.436 5.484 5.448 ... 0.0 0.0 0.0\n",
-                                          "    orientmat            (earth, inst, time) float32 0.0832 0.155 ... -0.7065\n",
-                                          "Attributes: (12/39)\n",
-                                          "    inst_make:                   Nortek\n",
-                                          "    inst_model:                  Vector\n",
-                                          "    inst_type:                   ADV\n",
-                                          "    rotate_vars:                 ['vel']\n",
-                                          "    n_beams:                     3\n",
-                                          "    profile_mode:                continuous\n",
-                                          "    ...                          ...\n",
-                                          "    recorder_size_bytes:         4074766336\n",
-                                          "    vel_range:                   normal\n",
-                                          "    firmware_version:            3.34\n",
-                                          "    fs:                          32.0\n",
-                                          "    coord_sys:                   inst\n",
-                                          "    has_imu:                     0
" - ], - "text/plain": [ - "\n", - "Dimensions: (x: 3, x*: 3, time: 122912, dir: 3, beam: 3, earth: 3,\n", - " inst: 3)\n", - "Coordinates:\n", - " * x (x) int32 1 2 3\n", - " * x* (x*) int32 1 2 3\n", - " * time (time) datetime64[ns] 2012-06-12T12:00:02.968749284 ...\n", - " * dir (dir) : Nortek Vector\n", - " . 1.07 hours (started: Jun 12, 2012 12:00)\n", - " . inst-frame\n", - " . (122912 pings @ 32.0Hz)\n", - " Variables:\n", - " - time ('time',)\n", - " - vel ('dir', 'time')\n", - " - orientmat ('earth', 'inst', 'time')\n", - " - heading ('time',)\n", - " - pitch ('time',)\n", - " - roll ('time',)\n", - " - temp ('time',)\n", - " - pressure ('time',)\n", - " - amp ('beam', 'time')\n", - " - corr ('beam', 'time')\n", - " ... and others (see `.variables`)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds_dolfyn = ds.velds\n", "ds_dolfyn" @@ -631,19 +118,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Percent of data containing spikes: 0.73%\n" - ] - } - ], + "outputs": [], "source": [ "# Clean the file using the Goring+Nikora method:\n", "mask = api.clean.GN2002(ds['vel'], npt=5000)\n", @@ -678,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -702,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -715,20 +194,24 @@ "metadata": {}, "source": [ "## Averaging Data\n", - "The next step in ADV analysis is to average the velocity data into time bins (ensembles) and calculate turbulence statistics. There are a couple ways to do this, and both of these methods use the same variable inputs and return identical datasets.\n", + "The next step in ADV analysis is to average the velocity data into time bins (ensembles) and calculate turbulence statistics. These averaged values are then used to calculate turbulence statistics. There are two distinct methods for performing this operation, both of which utilize the same variable inputs and produce identical datasets.\n", "\n", - "1. Define an averaging object, create a binned dataset and calculate basic turbulence statistics. This is done by initiating an object from the `ADVBinner` class, and subsequently supplying that object with our dataset.\n", + "1. **Object-Oriented Approach** (standard): Define an 'averaging object', create a dataset binned in time, and calculate basic turbulence statistics. This is accomplished by initiating an object from the ADVBinner class and then feeding that object with our dataset.\n", "\n", - "2. Alternatively, the functional version of ADVBinner, `calc_turbulence`.\n", + "2. **Functional Approach** (simple): The same operations can be performed using the functional counterpart of ADVBinner, turbulence_statistics.\n", "\n", - "Function inputs shown here are the dataset itself; \"n_bin\", the number of elements in each bin; \"fs\", the ADV's sampling frequency in Hz; \"n_fft\", optional, the number of elements per FFT for spectral analysis; \"freq_units\", optional, either in Hz or rad/s, of the calculated spectral frequency vector.\n", + "Function inputs shown here are the dataset itself: \n", + " - `n_bin`: the number of elements in each bin; \n", + " - `fs`: the ADV's sampling frequency in Hz; \n", + " - `n_fft`: optional, the number of elements per FFT for spectral analysis; \n", + " - `freq_units`: optional, either in Hz or rad/s, of the calculated spectral frequency vector.\n", "\n", - "All of the variables in the returned dataset have been bin-averaged, where each average is computed using the number of elements specified in \"n_bins\". Additional variables in this dataset include the turbulent kinetic energy (TKE) vector (\"ds_binned.tke_vec\"), the Reynold's stresses (\"ds_binned.stress\"), and the power spectral densities (\"ds_binned.psd\"), calculated for each bin." + "All of the variables in the returned dataset have been bin-averaged, where each average is computed using the number of elements specified in `n_bins`. Additional variables in this dataset include the turbulent kinetic energy (TKE) vector (\"ds_binned.tke_vec\"), the Reynold's stresses (\"ds_binned.stress\"), and the power spectral densities (\"ds_binned.psd\"), calculated for each bin." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "scrolled": true }, @@ -759,13 +242,17 @@ "\n", "For instance, \n", "- `do_var` calculates the binned-variance of each variable in the raw dataset, the complementary to `do_avg`. Variables returned by this function contain a \"_var\" suffix to their name.\n", + "- `calc_ti` is calculated from the ratio of the standard deviation of the horizontal velocity magnitude (equivalent to the RMS of turbulent velocity fluctuations) to the mean of the horizontal velocity magnitude\n", + "- `calc_psd` calculates the power spectral density (velocity spectra) of the velocity vector\n", "- `calc_csd` calculates the cross spectral power density between each direction of the supplied DataArray. Note that inputs specified in creating the `ADVBinner` object can be overridden or additionally specified for a particular function call.\n", - "- `velds.I` is the shortcut for turbulence intensity. This particular shortcut requires a dataset created by `do_avg`, because it requires bin-averaged data to calculate.\n" + "- `calc_epsilon_LT83` uses the Lumley and Terray 1983 algorithm to estimate the TKE dissipation rate from the isoropic turbulence cascade seen in the spectral. This requires the frequency range of the cascade as input.\n", + "- `calc_tke` calculates the TKE (Reynolds normal stress) components\n", + "- `calc_stress` calculates the Reynolds shear stress components\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "scrolled": true }, @@ -774,16 +261,21 @@ "# Calculate the variance of each variable in the dataset and add to the averaged dataset\n", "ds_binned = binner.do_var(ds, out_ds=ds_binned) \n", "\n", + "# Calculate the turbulence intensity\n", + "ds_binned[\"TI\"] = binner.calc_ti(ds.velds.U_mag)\n", + "\n", "# Calculate the power spectral density\n", "ds_binned['auto_spectra'] = binner.calc_psd(ds['vel'], freq_units='Hz')\n", - "# Calculate dissipation rate from isotropic turbulence cascade\n", - "ds_binned['dissipation'] = binner.calc_epsilon_LT83(ds_binned['auto_spectra'], ds_binned.velds.U_mag, freq_range=[0.5, 1])\n", "\n", "# Calculate the cross power spectral density\n", "ds_binned['cross_spectra'] = binner.calc_csd(ds['vel'], freq_units='Hz', n_fft_coh=512)\n", "\n", - "# Calculated the turbulence intensity (requires a binned dataset)\n", - "ds_binned['TI'] = ds_binned.velds.I" + "# Calculate dissipation rate from isotropic turbulence cascade\n", + "ds_binned['dissipation'] = binner.calc_epsilon_LT83(ds_binned['auto_spectra'], ds_binned.velds.U_mag, freq_range=[0.5, 1])\n", + "\n", + "# Calculate the Reynolds stresses\n", + "ds_binned['tke_vec'] = binner.calc_tke(ds[\"vel\"])\n", + "ds_binned['stress_vec'] = binner.calc_stress(ds[\"vel\"])" ] }, { @@ -795,30 +287,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Streamwise Direction')" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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ygIESERERkQMMlIiIiIgcYKBERERE5AADJTesWLECaWlpGDx4sK+XQkRERE1IEEVR9PUi/FVZWRkiIyNRWlrKTXHtOJRVjM/2X8K0vskY0y3e18shIiIC4Nrvb0UzrYlakdNXyvHKptPYfCIPAPDzyTzs/st4BCnlPl4ZERGRaxgokddcKqrC8s1nsOFwNkQRkAlAiEqBwspafHHwMuYO6+DrJRIREbmEPUrksfzyGjzzzTGMf3Ubvko3BEnT+iRh05IxeHRKdwDAuzsuQKvT+3ilRERErmFGidxWWl2Hd3acxwe7LqK6TgcAGNU1Do9N6YE+bSMBAG2igvHvLWeRVVSFH49dwfR+Kb5cMhERkUsYKJHLqmt1WPPrRazafh6l1XUAgAHto/DYlB4Y3jnW4thglRzzhqdi+c9nsGr7edzQNxmCIPhi2URERC5joEROq9Pp8emBS/jPlrPIL9cAALolhmHp5O6YlJboMAC6Y3gHrNp+HsdzyrDrXAFGdeUdcERE5B8YKFGj9HoR//s9B69tPoOsoioAQNvoYDw8qRtm9m8DuazhDFF0qAp/GNIOq3dfxMpt5xkoERGR32CgRA6JoohfTuXj5Y2ncepKOQAgLkyNP4/vgtuHtIdK4fy9AHeP6oS1ezLx6/lCHLlcgr5to5po1URERN7DQIns2nehEMs2nsbBzGIAQHiQAgvHdMadI1MRonL9Y9MmKhgz+qXgq/RsrNp+Hm/9aaC3l0xEROR1DJTIwrHsUry88TS2n7kKAAhSyjB/REcsHNMJUSEqj85935jO+Co9Gz8eu4KMgkp0jAv1xpKJiIiaDAMlAgBkFFTi1U2n8d2RXACAQibgD0Pa4c/juyIxIsgr1+ieFI7xPRLwy6l8vLPjAl6a1ccr5yUiImoqDJRaudzSaryx5Sw+/+0ydHoRggDM6JeChyd1Q4dY72d8Fo7pjF9O5WP9octYMqkrEsK9E4QRERE1BQZKrVRxZS3e2nYOH+7JRK3WMDF7Qo8ELJ3SHT2Tm26D38Gp0bimfRQOZZVg9e6LePy6Hk12LSIiIk8xUGplKjRavL8zA+/uvIAKjRYAMKRjDB6b0h2DUmOa/PqCIGDhmM64d+1B/HdvJu4f2xnhQcomvy4REZE7GCi1EhqtDh/vzcKKredQWFkLAOiVEoFHp3THmG7xzTote2LPRHRJCMO5/Ap8si8L943p3GzXJiIicgUDpQCn1enxVXo2/v3zWWSXVAMAOsaF4uFJ3XB9n2TIGhkW2RRkMgH3ju6Ex748gvd3ZWD+yFSoFfJmXwcREVFjGCgFKFEU8dOxK3hl02mcv1oJAEiKCMLiiV0xe2BbKOXOD4tsCjf2b4PXNp3BlbIafJ2ejdsGt/fpeoiIiOxhoBSAdp0twLKNp3DkcikAICpEifvHdsYdw1MRpGwZmRuVQoYF13bEP344ibd3XMAtA9v5JLtFrhFFEXoRjW5bQ0QUKBgoBZDDl0qw7KdT+PV8IQAgRCXH3dd2xN2jOyGiBTZM3z60Pf7zy1lcuFqJTSfycF3vJF8viRqg14u46a3dKK2uw//+fG2L/EwREXkbA6UAcCavHK9sPI1NJ/IAACq5DH8a1h6LxnVBXJjax6tzLEytwNzhHbBi63ms2n4eU3olNmtTObnmUFYxfjdmKd/fmYElk7r5eEVERE2PgZIfu1RUhdd/PosN6ZehFwGZANx8TVssntgVbaNDfL08p8wf0RHv7szA4Usl2JdRhGGdYn29JHLgx2NXTH//YFcG5o9IRXSoZ9vaEBG1dAyU/NDVcg1WbD2Hj/dlok4nAgCu65WEpVO6oUtCuI9X55r4cDVuGdgWH+/Lwqrt5xkotVDSzQEAEKqSo1yjxTs7L3BgKBEFPN/e+kQuKaupwysbT2PMy1ux5teLqNOJuLZLHL5ZNBKr5g70uyBJcu/oTpAJwLbTV3Eyt8zXyyE7jlwuRXZJNUJUciyb3Q8AsGb3RVwt1/h4ZURETYuBkh+ortVh1fbzGPWvrXhz6zlU1erQr10UPrl7KP5791D0axfl6yV6pENsKKb2SQYAvL39vI9XQ/b8cMywWfK4HgmY1icJ/dtFobpOh5Xb+N+LiAIbA6UWrE6nx8f7MjH2la3454+nUFpdh64JYXh77kB8ff8IjOgS5+sles3/Gadzf3skF5eKqny8GjJnXnab1jsZgiDgkcmGRu7/7stEbmm1L5dHRNSkGCi1QHq9iG8OZ2PSa9vx1IZjyCvToE1UMF69pR9+emg0pvRKCri7w3q3icS1XeKg04t4f1eGr5dDZk7kliGzsApqhQxju8cDAK7tEochHWNQq9XjzV/O+XiFRERNh4FSC/T8dyew+NPDuFhYhbgwFZ6dnoZflo7BzQPbBvSgv4XGrNKnB7JQZNyPjnzvx6OGbNLY7vEIVRvu/xAEAY8YxwN8duASs4BEFLAYKLVAswe2RUSQAksnd8P2R8dh/siOrWIvtJFdYtG7TQRq6vT48NeLvl4OwVB2k/qTphn7yCRDO8ViVNc4aPUi/r3lrC+WR0TU5BgotUC920Ri75MT8MD4rqZ/wbcGgiCYskof7rmIqlqtj1dEZ/MrcOFqJVRyGcb3SLB5/ZHJ3QEAXx26jPNXK5p7eURETY6BUgsVomo9AZK5qb2T0SE2BCVVdfjswCVfL6fVk8puo7rGIdzOliX920VhYs8E6EXg3z8zq0REgYeBErUocpmAe0Z1AgC8tzMDdTq9j1fUuv1oLLs1tA+ftJXJt0dycOoK52ARUWBhoEQtzuyBbREXpkJ2STW+O5Lj6+W0WheuVuDUlXIoZAImpSU6PK5XSiSu75MMUQSWbz7TjCskImp6DJSoxQlSynHnyI4AgLe3X4Aoij5eUesk7e02okscokIa3tNt8cSuAIDNJ/LYW0ZEAYWBErVIc4Z2QKhKjlNXyrHt9FVfL6dVkspuUxsou0m6JYYjLkwNvQicvlLe1EsjImo2DJTcsGLFCqSlpWHw4MG+XkrAigxR4o9D2wMAVnJbk2Z3qagKx7LLIBOAyQ2U3cylpUQAMAyoJCIKFAyU3LBo0SKcOHECBw4c8PVSAtqCaztBKRewP6MIh7KKfb2cVkXKJg3rFIvYMLVT70lLNgZKOQyUiChwMFCiFispMgg39m8DAFjFzVeb1Q/GsQDOlN0kzCgRUSBioEQt2n1jDKMCNp/Mw7l8DjRsDjkl1Th8qQSCAEzp5UKgZMwonb5SDp2eDfhEFBgYKFGL1iUhHJPSEiGKwDs7mFVqDj8Z73Yb1CEaCRFBTr+vY1wogpQyVNXqkFlY2VTLIyJqVgyUqMWTtjXZkJ6NK6U1Pl5N4JMCpam9kxs50pJcJqB7EstvRBRYGChRizewQzSGpMagTifig90Zvl5OQMsvr8GBzCIADU/jdoQN3UQUaBgokV9YONbQq/TJviyUVtf5eDWBa+PxPIiiYQ+3lKhgl9/Phm4iCjQMlMgvjOuegO6J4ajQaPHfvZm+Xk7A+vawYcuYaX1czyYBzCgRUeBhoER+QRAE0x1wq3dfRE2dzscrCjyXiqqw/2IRBAGY3i/FrXP0SAqHIAD55RpcLdd4eYVERM2PgRL5jen9UtAmKhgFFRqsP3TZ18sJON8czgYAjOgci+RI18tuABCqVqBjbCgA4CTLb0QUABgokd9QymVYcK1hs9x3d1zgrB4vEkURX6UbAiVpyKe7erJPiYgCCAMl8it/GNIOUSFKXCysMt3GTp47crkUF65WIkgpw9Q+ro0FsCb1KTGjRESBgIES+ZUQlQJ3DE8FAKzafh6iyKySN2wwZpMmpyUhTK3w6FymO9/Y0E1EAYCBEvmd+SNSEaSU4Wh2KX49X+jr5fi9Op0e3/5uuNvtpgGeld2A+ozS+asVbLonIr/HQIn8TkyoCrcNagfAkFUiz+w6W4DCylrEhqowqmucx+dLCFcjNlQFvWjY942IyJ8xUCK/dPeoTpDLBOw8W4Bj2aW+Xo5fk5q4p/dLgULu+Y8EQRA4eJKIAgYDJfJL7WJCcENfQ9Mxs0ruK6+pw6bjhqb4Wdd4XnaTcPAkEQUKBkrkt+4bbdgs94ejudyt3k0/HbsCjVaPzvGh6NMm0mvnZUaJiAIFAyXyW2kpERjTLR56EXh35wVfL8cvSXe73TSgDQRB8Np5zUcE6Dnvioj8GAMl8msLxxiySl/8dhkFFdwywxW5pdXYc8Fw1+BMD4dMWusYFwq1QoaqWh0yi6q8em4ioubEQIn82rBOMejXLgoarR5rdl/09XL8yjeHcyCKwJDUGLSLCfHquRVyGXokhQPg4Eki8m8MlMivCYKA/zNulvvRnouo0Gh9vCL/IIoiNhwylt282MRtjoMniSgQMFAivzcpLQmd4kJRVqPFp/uzfL0cv3Aytxyn88qhksswrbdnW5Y40jOZDd1E5P8YKJHfk8sE3DvakFV6b2cGarV6H6+o5duQfhkAMKFnAiJDlE1yDY4IIKJAwECJAsJN17RBQrgaV8pq8M3hbF8vp0XT6UV8c9h7W5Y40sMYKF0pq0EhG+2JyE8xUKKAoFbIcde1HQEAb++4wFvSG/Dr+QLkl2sQFaLE2O4JTXadMLUCqbGGJvGTudzKhIj8EwMlChh/HNoe4WoFzuVXYMupfF8vx2O1Wj3Ka+q8fl6pifuGvslQKZr2R0D94EluM0NE/omBEgWMiCAl/jSsAwDDtiai6N9Zpdvf3YtBL/yMd3ach85LGbKqWi1+Mm5Z0pRlNwn7lIjI3zFQooBy18hUqBQyHMwsxtbT/ptVyi2txsHMYmi0erz4wynMXvUrzuVXeHzeTcfzUFWrQ/uYEFzTPtoLK20YtzIhIn/HQIkCSkJEEO4ckQoAePGHU9Dq/PMOuH0XigAACeFqhKkVSM8qwbQ3duLt7Z5ll6QtS2708pYljqQlG/aPO3+1EjV1uia/HhGRtzFQooBz/7guiA5R4lx+BdYduOTr5bhlX4Zha5EbB7TBxiWjMbpbPGq1erz04yncvPJXnMt3rTk6PasYC9cexPYzVwE0T9kNABIj1IgJVUGnF3E2z/OMGBFRc2OgRAEnMliJhyZ2AwC8vvlMkzREN7W9xozS0I4xaBMVjA/vHIxlN/dFuFqBw5dKMO2NXVi1/XyDGTNRFLH1dD5ue3sPbnrrV1Nv0p0jU9ExLrRZvg5BENDeuD1KTml1s1yTiMibGChRQPrj0PboFB+KwspavLXtvK+X45IrpTXIKKiETAAGpcYAMAQctw5uh00Pj8YYY3bpnz+ews2r9uBsnmV2qU6nx9fp2Zj67524c/UB7MsogkIm4OZr2mLTktH42/Rezfr1xISqAAAlVbXNel0iIm9goEQBSSmX4YmpPQEA7+/KwOVi/9nBXiq79UqJRGSw5dTs5MhgrLlzMJbN7ovwIAV+v1SC69/Yhbe2nUN5TR1W787A2Je34aHPDuPUlXKEquS4+9qO2PHYOLx6az90Swxv9q8nyjj5u6jS/zJ7REQKXy+AqKlM7JmAYZ1isPdCEV7eeBr//sMAXy/JKXsvGAKlYZ1i7L4uCAJuHdQOo7rG4cmvjmLr6atY9tNpvLbpDLTGRu/YUBXuHJmKucNSm2yLEmdFhzCjRET+ixklCliCIODp69MgCMA3h3Nw+FKJr5fkFKk/aVin2AaPS44MxgfzB+OVW/ohPEgBrV5E+5gQ/P3G3tj9l/F4YHxXnwdJQH3prZiBEhH5IWaUKKD1bhOJmwa0wVeHsvGP70/g8/uGN8tt8e7KK7PtT2qIIAiYPbAtxnSLx5m8cgztGAOFvGX9+4elNyLyZy3rJypRE3h0SncEKWU4cLEYG413frVUUtnNXn9SQ+LD1RjZJa7FBUkAS29E5N9a3k9VIi9LjgzGPaM6AQD++eMp1Gpb7hBK87EAgUIKlFh6IyJ/xECJWoX7xnRGXJgaFwur8N+9mb5ejkP7TI3cDfcn+ZPoUENmrLiKpTci8j8MlKhVCFMr8MhkwxDKN345i9IW+Es7r6wGFwoqIQjA4ADMKJVU1ULvpc19iYiaCwMlajVuHdQO3RPDUVJVh//8ctbXy7FR358U4VJ/UksnNXPrRaC8Ruvj1RARuYaBErUacpmAJ683DKH8cM9FZBZW+nhFlkxjAToGTtkNANQKOUJVcgBAEfuUiMjPMFCiVmVMt3iM7haPOp2If/10ytfLsSBN5A6k/iRJFBu6ichPMVCiVuepaT0hE4Afjl7BbxeLfL0cAEB+WQ0uXA28/iSJaehkJQMlIvIvbg2c/N///ufyeyZNmoTg4GB3LkfkVd2TwnHb4HZYt/8SXvj+JDbcP8LnQyj3ZhgCtkDrT5JIfUq8842I/I1bgdKNN97o0vGCIODs2bPo1KmTO5cj8rolk7rhf8ZtTb49kosZ/VJ8uh6pkXtogPUnSaSMEodOEpG/cbv0duXKFej1eqf+hISEeHPNRB5LCA/CwjGdAQD/+vEUaup0Pl3P3gCcn2ROGhFQxNIbEfkZtwKlefPmuVRGmzNnDiIiIty5VIu0YsUKpKWlYfDgwb5eCnng7lGdkBQRhOySaqz59aLP1mHenzTEif3d/BFLb0Tkr9wKlFavXo3w8HCnj1+5ciXi4uLcuVSLtGjRIpw4cQIHDhzw9VLIA8EqOR6d0h0AsOKXcyis0PhkHVJ/UlpyBCJDAq8/CWDpjYj8l8d3vY0fPx7PPfeczfPFxcUYP368p6cnalI3DWiD3m0iUK7R4t9bfDOEMhC3LbEWxdIbEfkpjwOlbdu24c0338SNN96Iysr6AX61tbXYvn27p6cnalIymYCnpqUBAD7el4Vz+RXNvoZA708CgGhjpqyEpTci8jNemaP0888/48qVKxg2bBguXrzojVMSNZvhnWMxsWcidHoR//zxZLNeO7+8BucDvD8JqG/m5sBJIvI3XgmUkpOTsX37dvTt2xeDBw/Gtm3bvHFaombzxLQeUMgE/HwyH7+eL2i26+4zblvSMylw+5MAIDq0PlASRW6MS0T+w+NASRrUp1ar8fHHH2Px4sW47rrr8NZbb3m8OKLm0jk+DH8a2h4A8I/vTzbbLvetoewG1Jfe6nQiKmt9O4qBiMgVHgdK1v86fPrpp/Hxxx/j1Vdf9fTURM1q8cRuCA9S4HhOGb5Kz26Wa9YHSoFbdgOAYKUcaoXhxw23MSEif+JxoJSRkWFz6//NN9+MvXv34oMPPvD09ETNJiZUhQfGdQEAvLLxNKqbOPNxtVxT358UgPu7mRMEgX1KROSX3A6UysrKUFZWhujoaFRUVJgeS3/at2+Pm266yZtrJWpy80akom10MK6U1eDdnRea9Fr7MgzZpJ5JEabb5wMZh04SkT9ya683AIiKimpwI1FRFCEIAnQ69iOQ/whSyvH4dT3w53XpWLX9PP4wuB0SIoKa5FqtpT9JwqGTROSP3A6Utm7davq7KIqYNm0a3nvvPbRp08YrCyPylRv6JuOD3RlIzyrBa5vP4J83922S6+w13vEW6P1JEu73RkT+yO1AacyYMRaP5XI5hg0bhk6dOnm8KCJfEgQBT1/fEzev3IPPf7uE+SNT0SPJu3sVXi3X4Fx+RavoT5Kw9EZE/sgrc5SIAs3ADjG4vk8y9KJhXIC3Sf1JPVpJfxLA0hsR+ScGSkQOPH5dD6jkMuw8W4Btp/O9eu7WMhbAHPd7IyJ/5NVAqaHmbiJ/0z42BPNGdAAAvPjDSWh1eq+de5+pP6l1NHID3O+NiPyT2z1Ks2bNsnhcU1ODhQsXIjQ01OL5r776yt1LEPncA+O64ouDl3EmrwKf/XYJfxraweNzFlRocNbYnzS0lfQnAZbbmBAR+Qu3A6XIyEiLx3PmzPF4MUQtTWSIEosndMVz357A3787gTZRwRjbPcGjc0rZpNbUnwSYbYzL0hsR+RG3A6XVq1d7cx1ELdacYR2w82wBfjmVj3s++g2v3zYA1/dNdvt8rbE/CagvvfGuNyLyJ273KD355JPYv3+/N9dC1CIp5TK8PXcgpvdLQZ1OxJ/XHcJnB7LcPl9rGzQpkUpv1XU61NRxEC0R+Qe3A6Xc3FzccMMNSE5Oxr333ovvv/8eGo3Gm2sjajGUchlev60//ji0PfQi8Pj6o3jPjS1OpP4kABiS2roySuFqBRQyww0f7FMiIn/hdqC0evVq5OXl4fPPP0dUVBQeeeQRxMXFYdasWVizZg0KCgq8uU4in5PLBPzjxt64b4xhqOoL35/Ea5tOQxRFp8+xP0PqTwo3ZVhaC0EQTD1ZxZUsvxGRf/BoPIAgCBg1ahSWLVuGU6dOYf/+/Rg2bBjeffddtGnTBqNHj8Yrr7yC7Oxsb62XyKcEQcATU3viseu6AwDe+OUcnvv2BPR654Kl1lp2k9SPCGBGiYj8g1fnKPXs2ROPPfYYdu/ejcuXL2PevHnYuXMn1q1b583LEPnc/WO74O8zewEA1vx6EUu//N2pOUsMlIxDJxkoEZGfcCtQ2rx5MyoqDH0Wb731Fu69916cOnXK4pj4+HgsWLAA33zzDZYuXer5SolamLnDU7H8tn6QywR8dSgb9398CBqt4yblggoNzuQZ/n/TmuYnmYsO5Z1vRORf3AqUli5dirCwMOzduxcffvghxo4diwULFnh7bUQt3k0D2mLVnIFQKWTYdCIPC9b8hkqN1u6xrbk/SSJllEo4S4mI/IRHpbevv/4af/7zn/HHP/4RVVVV3loTkV+ZlJaINfMHI0Qlx65zBZjz/j6U2smYtPayG2C23xtLb0TkJ9wKlFJSUjB37lx8+umnuP7666HRaKDTcS4KtV4jusTh47uHIjJYifSsEtz2zh7kl9dYHNNaB02aiwnlfm9E5F/cCpS+/PJL3HTTTdi8eTOio6NRVFSEV155xdtrI/IrA9pH4/P7hiM+XI1TV8px66o9uFxsyLQWmvUnDenIjBLnKBGRv3ArUAoNDcWsWbPQtWtXAEBycjImT57s1YUR+aPuSeH4cuFwtI0OxsXCKtyyag/O5VdY9CfFtNL+JID7vRGR//F4PEB1dbVFf1JmZiZef/11bNq0ydNTE/mlDrGh+HLhCHRJCENuaQ1ufXsPPtlv2PKkNfcnAfWlN971RkT+wuNAaebMmfjoo48AACUlJRg6dCheffVVzJw5EytXrvR4gUT+KCkyCJ/fNxx92kSiqLIWO88aJtW35v4kgKU3IvI/HgdKhw4dwqhRowAYepcSExORmZmJjz76CG+88YbHCyTyVzGhKnxyz1AMMZuZ1Jr7k4D60lt5jRZ1TgzoJCLyNYWnJ6iqqkJ4eDgAYNOmTZg1axZkMhmGDRuGzMxMjxdI5M/Cg5T46K4h+OePp5AUGdSq+5MAIDJYCUEARNFw51t8uNrXSyIiapDHGaUuXbrg66+/xqVLl7Bx40ZTU3d+fj4iIiI8XiCRvwtSyvHsjF5YOKazr5fic3KZgMhgqU+J5Tciavk8DpSeeeYZLF26FKmpqRgyZAiGDx8OwJBdGjBggMcLJKLAwjvfiMifuF16e/LJJ3HjjTdi9uzZuPbaa5Gbm4t+/fqZXp8wYQJuuukmryySiAJHdIgSGeCdb0TkH9wOlHJzc3HDDTdALpdj+vTpmDlzJtLS0qBWG3oOhgwZ4rVFElHgiOadb0TkR9wuva1evRp5eXn4/PPPERUVhUceeQRxcXGYNWsW1qxZg4KCAm+uk4gCBEcEEJE/8ahHSRAEjBo1CsuWLcOpU6ewf/9+DBs2DO+++y7atGmD0aNH45VXXkF2dra31ktEfo77vRGRP/G4mdtcz5498dhjj2H37t24fPky5s2bh507d2LdunXevAwR+TEpo1TEZm4i8gMez1FyJD4+HgsWLMCCBQua6hJE5IekWVIlLL0RkR9wOaNUXV1tt5R2/PhxryyIiAJbdAj3eyMi/+FSoPTll1+iW7dumDZtGvr27Yt9+/aZXps7d67XF0dEgSeKc5SIyI+4FCi98MILOHToEH7//Xd88MEHuOuuu/DJJ58AAERRbJIFElFgkUpvvOuNiPyBSz1KdXV1iI+PBwAMGjQIO3bswKxZs3Du3DkIgtAkCySiwBJlLL2VVtdBpxchl3n+s0MURVRotAgPUnp8LiIicy5llBISEnDkyBHT49jYWGzevBknT560eJ6IyJGoYENGSS8CZdXe6VN6Y8s59Hl2E3ad5fw2IvIulwKltWvXIiEhweI5lUqFdevWYfv27V5dGBEFJpVChnC1IZntjfJbhUaLd3deAADsPHvV4/MREZlzqfTWtm1bh6+NHDnS48UQUesQFapEuUbrlTvfNhy6jAqNFgCQWVjl8fmIiMx5ZY5STU0Njhw5gvz8fOj1eovXZsyY4Y1LEFEAiQ5R4VJRtcd3vomiiA/3ZJoeXyys9HRpREQWPA6UfvrpJ9xxxx1293YTBAE6nc7TSxBRgPHWxrh7LhTiXH4FZIKh5ymrqAqiKPLmEiLyGo+3MHnggQdwyy23IDc3F3q93uIPgyQiskcaOunpfm8f/WrIJt0ysB1kAlBVq8PVCo3H6yMikngcKOXn5+Phhx9GYmKiN9ZDRK2Aab83DzJK2SXV2HTiCgBgwaiOSI4MBgBkudinVFJVi6Vf/I5fz/GOOSKy5XGgNHv2bGzbts0LSyGi1sIb+719si8TehEY3ikW3RLDkRoXAgC46GKgtOl4Hr48eBnLNp52ey1EFLg87lF68803ccstt2Dnzp3o06cPlErLgW8PPvigp5cgogBj2u+t0r3SW02dDuv2XwIAzBvRAQDQITYUu88VIsvFhu7c0hoAwImcMmi0OqgVcrfWRESByeNA6ZNPPsHGjRsRHByMbdu2WTRRCoLAQImIbHhaevvhaC6KKmuRHBmEiT0NZf8OMe5llPLLDYFSrU6P4zlluKZ9tFtrIqLA5HGg9PTTT+P555/HX/7yF8hkHlfyiKgV8LT0Jo0EmDOsAxRyw8+dDrGhAIBMFzNKeWX1zd/pWSUMlIjIgseRTW1tLW677TYGSUTkNGm/N3cGTp7Lr8Dvl0qgkAm4bXA70/MdYg0Zpcwi9zJKAJCeVezyeogosHkc3cybNw+fffaZN9ZCRK2EaY5SZS1EUXTpvT8czQUAXNs1DnFhatPzUqBUUlWHUhcCsHyrjBIRkTmPS286nQ7Lli3Dxo0b0bdvX5tm7tdee83TSxBRgIkJVUEQAK1exNUKDRLCg5x+rxQoTeudbPF8iEqB+HA1rpZrkFlUib4hUY2eS2e8viS7pBr5ZTVIiHB+PUQU2DwOlI4ePYoBAwYAAI4dO2bxGqfjEpE9QUo5uieG49SVchy8WIypfZIbfxOA81crcOpKORQyAZN72c5uS40NwdVyDS4WVqFv26hGz1dYqYFOL0ImAJ3iw3AuvwLpl0owpVeSq18SEQUojwOlrVu3emMdRNTKDOkYg1NXyrH/YpHTgdKPxmzSiC5xpjvnzLWPCcWBi8VOjwiQym5xYWoMTo02BEpZDJSIqJ5bPUpHjhyx2fy2IcePH4dWq3XnUkQUoAanxgAA9mcUOf2e748aJnFf38d+IJMa69qIAKmROyFCjQHtDHe7saGbiMy5FSgNGDAAhYWFTh8/fPhwZGVluXMpIgpQQzoaAqUTuWUoq2m8+TqjoBInc8sglwmYnGY/UGpvDJSc3cZEGg2QGB6EAe2jAABHLpdCq3P+H4JEFNjcKr2Jooi//vWvCAkJcer42lrPdggnosCTGBGE1NgQXCyswsHMYozrntDg8VIT94jOsYgOtS27AUCqcZbSRSdLb3llUkYpCJ3jwxAepEB5jRan88rRKyXS2S+FiAKYW4HS6NGjcfq08/siDR8+HMHBwe5ciogC2ODUGFwsrML+jCKnA6VpDfQzSSMC8ss1qKrVIkTV8I+4/HJDRikhXA2ZTED/dlHYebYAh7JKGCgREQA3AyVugktE3jCkYwy+OHi50T6lzMJKHM8xlN0aarSOClEhMliJ0uo6ZBVVoUdSRIPnzTdmlBKN4wAGtI/GzrMFSM8qxtxhHVz8aogoEHGcNhH5zNCOsQCAI5dLUFOnc3jc98Zs0vBOsabtTxyRGroznehTMvUoRRgGV0p9Soc5eJKIjBgoEZHPtIsJRmKEGnU6scGp2M6U3STtXdjzzXTXm3Hg5YB2UQCACwWVTjWYE1HgY6BERD4jCAKGGLNKBy7aL79lFVbhWLZUdrMdMmnN2YySTi/iarllRkkq3QFAXmmNw/cSUevBQImIfGpIqmF+kaM+JansNqxTDGLN9nZzpH2Mc4FSYYUGehGQCbA4b0K44e9SozcRtW4MlIjIp6SM0sHMYtRZzS/S6vRYt98wg+36PilOnS81zlh6K2q49CYFQnFhashl9dstJURIgRIzSkTkhUBp/vz52LFjhzfWQkStUNeEMESFKFFdp8PxnDKL1747kousoirEhKpw4wDnAqUOxoxSdnE1arWOB0fmWd3xJpH6laTtTYiodfM4UCovL8fkyZPRtWtXvPjii8jOzvbGuoiolZDJBAzqIG1nUj/xX68XsWLrOQDAXSNTG52JJIkPVyNYKYdeBLJLqh0eJ93xJpXaJCy9EZE5jwOl9evXIzs7Gw888AC++OILpKamYurUqfjyyy9RV8e7RoiocUM72u77tulEHs7mVyBcrcDc4alOn0sQBNPgyYYmdNfv82aZUYp3IlDS6UVUaLh/JVFr4JUepdjYWCxevBjp6enYv38/unTpgrlz5yIlJQVLlizB2bNnvXEZIgpQ0r5vBy4WQ68XIYoi3tpmyCbdMaKD6U40Z0mBUmaB40DJeoaSRAqcpGGU9vxl/RH0e24TzuVXuLQuIvI/Xm3mzs3NxaZNm7Bp0ybI5XJMmzYNx48fR1paGpYvX+7NSxFRAOmVEoEQlRyl1XU4k1+OnWcLcORyKYKUMtw1sqPL5+sgzVIqcnznmxQIST1JEqn0dtVBRulycRXWH7oMnV7Eoaxil9dGRP7F40Cprq4O69evxw033IAOHTrgiy++wJIlS5Cbm4sPP/wQmzZtwtq1a/H88897Y71EFIAUchkGdqgfE/CmsTfp9iHtnRoJYK1NlGFvydwSx1mh/HIHGaVGSm/r9mdBLxr+XlDBPiaiQOfWXm/mkpOTodfrcfvtt2P//v3o37+/zTFTpkxBVFSUp5ciogA2ODUGO88WYM3ui7hQUAmlXMC9ozu5da7kSEOWKKe0oWZuB3e9GR9XaLSo1GgRqq7/MVmr1eOzA5dMjx1lnYgocHgcKC1evBiPPPIIQkJCLJ4XRRGXLl1C+/btER0djYyMDE8vRUQBTOpTumDsK5o9sC2SI4PdOleKMaOU4yCjpNOLpmyQ9V1vYWoFQlRyVNXqkF+uQUezQGnj8SsoqKg1PTb/OxEFJo9Lb88++ywqKmwbGouKitCxo+u9Bc3tpptuQnR0NGbPnu3rpRC1av3bRUElN/xIkgnAwjGd3T6XlFEqqNBAo7XdbNfRVG6Jqfxm1dD9372ZAIBOxqGWBcwoEQU8jwMlURTtPl9RUYGgoCC7r7UkDz74ID766CNfL4Oo1QtSytG3bSQAYEa/FFNDtjtiQlVQKww/3vJKbYMZ6Y63+HDLqdwS09BJs0DoTF459mUUQS4TcP+4LgDYo0TUGrhdenv44YcBGGaWPPPMMxalN51Oh3379tntV2ppxo0bh23btvl6GUQE4OFJ3bB2byYeu66HR+cRBAHJkUG4WFiFnNJqtI+1bA3Ic3DHmyQ+wrah+5N9hq1UJvRIQO82EQAYKBG1Bm5nlNLT05Geng5RFHH06FHT4/T0dJw6dQr9+vXDmjVrPFrcjh07MH36dKSkpEAQBHz99dc2x7z11lvo2LEjgoKCMHDgQOzcudOjaxKR74zoEoeVcwaaeow8IfU35dpp6HZ0x5uk/s63+tLbgYuGYZg3DWiDOGO5rriqzmZ/OiIKLG5nlLZu3QoAuPPOO/Hvf/8bERERXluUpLKyEv369cOdd96Jm2++2eb1zz77DA899BDeeustjBw5Em+//TamTp2KEydOoH379gCAgQMHQqOx/Vffpk2bkJLi3N5REo1GY3GusrKyBo4mIl9KjjLe+WanoduUUYqwn1GSMk1XjSU6nV40DZfskRyB6BAVZAKgF4GiylqbO+eIKHB4fNfb6tWrvbEOu6ZOnYqpU6c6fP21117DggULcPfddwMAXn/9dWzcuBErV67ESy+9BAA4ePCg19bz0ksv4bnnnvPa+Yio6aQ0mFGSSm+NZZQMgdLl4ipotHqoFTK0jwmBXCYgJlSNggoNrpZrGCgRBTC3AqWHH34Yf//73xEaGmrqVXLktddec2thjamtrcXBgwfxl7/8xeL5yZMn49dff22Saz7xxBMWX29ZWRnatWvXJNciIs9IGSV7QyfzTduXOMgoRViW3s7kGbJJnePDTM3fcWEqFFRo2KdEFODcCpTS09NNG96mp6c7PE4QbO8m8ZaCggLodDokJiZaPJ+YmIgrV644fZ4pU6bg0KFDqKysRNu2bbFhwwYMHjzY7rFqtRpqtetTgomo+UkZpZxSO6W3cmnYpKOMkuVdb2fyygEAXRPDTMfEh6tx6ko5ZykRBTi3AiWpP8n6775gHYyJouhSgLZx40ZvL4mIWgCpIdxe6U3KMjm6600qvZVU1UGj1Zn6k7olhpuOkRq6mVEiCmwez1Gqrq5GVVX9xpOZmZl4/fXXsWnTJk9P3aC4uDjI5XKb7FF+fr5NlomIWh+p9FZSVYfq2vqhk0WVtSisNGSBOsXbn9UUFaI0Db+8Wq6pzygl1GeU4sJUADh0kijQeRwozZw50zSwsaSkBEOGDMGrr76KmTNnYuXKlR4v0BGVSoWBAwdi8+bNFs9v3rwZI0aMaLLrEpF/iAhSIsy4/Yj5nm9SdqhtdDBCVPaT6oIgIN6YVcorqzG9pyszSkStjseB0qFDhzBq1CgAwJdffomkpCRkZmbio48+whtvvOHRuSsqKnD48GEcPnwYAJCRkYHDhw8jK8sw+O3hhx/Ge++9hw8++AAnT57EkiVLkJWVhYULF3p0XSIKDNJWJuYN3WfzbbND9kiB0qHMEmi0eqiMd7xJ6gMl9igRBTKPxwNUVVUhPNzwr6xNmzZh1qxZkMlkGDZsGDIzMz0692+//YZx48aZHkt3nM2bNw9r1qzBbbfdhsLCQjz//PPIzc1F79698cMPP6BDhw4eXZeIAkNyVDDO5ldYZJTO5tlmh+yR+pR2nSsAYHnHGwDEhTOjRNQaeBwodenSBV9//TVuuukmbNy4EUuWLAFg6BXydAjl2LFjHe4lJ7n//vtx//33e3QdIgpMKXYySlIZrUsjGSVpRMD+DMNE7m6JlsebepQYKBEFNI9Lb8888wyWLl2K1NRUDB06FMOHDwdgyC4NGDDA4wUSEbnL3jYmzpbepDviqusMjeDdrDJQ8cbSW1FlLXT6hv9BR0T+y+NAafbs2cjKysJvv/2Gn376yfT8hAkTsHz5ck9PT0TkNtM2JsZZSqXVdcgzDptsNKNkNbXb+viYUBUEs21MzGUVVuGF707gip0ZTkTkXzwuvQFAUlISkpKSLJ4bMmSIN05NROQ20zYmJYaMklR2S44MQniQssH3JlgNo7TOKCnkMkSHqFBUWYuCCo2p+RsA3t15AWv3ZkKtlOHRKT08/jqIyHe8Eiht2bIFW7ZsQX5+PvR6y520P/jgA29cgojIZfUb41ZDFEWcM5bdGssmAZbDKK3veJPEh6lNgZK5zCLDbLmLhVU27yEi/+Jx6e25557D5MmTsWXLFhQUFKC4uNjiTyBasWIF0tLSHG51QkQtg5RRqqzVoaxGW3/HW0LDd7wBlqU36zveJHHh9hu6LxcbAqTLRQyUiPydxxmlVatWYc2aNZg7d6431uMXFi1ahEWLFqGsrAyRkZG+Xg4RORCskiMqRImSqjrkllbjrGlwZOMZpdgwNWTGHiTrO94kpllK5fU9SqIoIrvYUOq7VGy7fQoR+RePM0q1tbWchE1ELZbpzrcSswnbTpTe5DIBscZAyNHx9qZzXy3XQKM1tCAUVdaiUqN1f/FE5HMeB0p33303PvnkE2+shYjI66RZSufyK5BtbOp2pkcJMGxzAgA9k+3PhJMCpatmgZJ1Fukys0pEfs3j0ltNTQ3eeecd/Pzzz+jbty+USss7SV577TVPL0FE5DapoXvH2asADFuTRIWonHrvs9N74dfzhRjbPcHu6/VDJ+tLb1J/kuRSURW6JzXeE0VELZPHgdKRI0fQv39/AMCxY8csXhME2+ZHIqLmJJXepAnbzpTdJP3aRaFfuyiHr5u2MSmvzyhZZ5AuFbOhm8ifeRwobd261RvrICJqEinGjJLUN+RKoNSYeDs9SlKgJDWCs/RG5N887lEiImrJpIySpEsjm+G6QupRKqyshd64jYlUeuuVYrgj9hJHBBD5Na8ESjt37sScOXMwfPhwZGdnAwDWrl2LXbt2eeP0RERuS7EKlLyZUYo19ijp9CJKqusAwDQaYHjnWAAcEUDk7zwOlNavX48pU6YgODgY6enp0GgMKejy8nK8+OKLHi+QiMgTiZGWW5F4M1BSymWmrUsuXK2AXi/isvHOuuGdDIHS5aIqiCI3zSXyVx4HSi+88AJWrVqFd9991+KOtxEjRuDQoUOenp6IyCNqhdxUIosJVZlmI3nL0I4xAICdZwtwtUKDWq0ecpmAQanRAIByjRZl1ZylROSvPA6UTp8+jdGjR9s8HxERgZKSEk9PT0TkMamh29n5Sa4Y1TUOALDz7FVTf1JShGHTXWl8QEu98+2L3y5h5D9/Me2BR0S2PA6UkpOTce7cOZvnd+3ahU6dOnl6eiIijyUbh056s+wmubZrPADg98ulOJFTBqB+UGXbaMNGui2xoVuvF/Hol0eQXVKNd3dk+Ho5RC2Wx4HSfffdh8WLF2Pfvn0QBAE5OTn4+OOPsXTpUtx///3eWCMRkUdGdomDIACju8V7/dxtooLROT4UOr2ILw5eBlAfILWLMQZKLTCjdDCrftPy+HDvliOJAonHc5Qee+wxlJaWYty4caipqcHo0aOhVquxdOlSPPDAA95YIxGRR+4Ynoqbr2mLULXHP/LsGtU1HuevVuLI5VIA9Rmldsb/NZ+lJIoivj6cja4J4ejdxneban9zOLt+TWCzOZEjXhkP8I9//AMFBQXYv38/9u7di6tXr+Lvf/+7N07dIq1YsQJpaWkYPHiwr5dCRE5qqiAJqO9TkjRUejudV44ln/2O+9Ye9NndcHU6Pb4/kmt6XF2r98k6iPyBRz859Ho91qxZg6+++goXL16EIAjo2LEjZs+ejblz5wbsFiaLFi3CokWLUFZWhshI3/2LkIhahmGdYqGUC6jTGQKf+tKbIWAyn6WUYxwfkF1SjfNXK5ukwbwx2cXVKK6qMz2uruNdeUSOuJ1REkURM2bMwN13343s7Gz06dMHvXr1QmZmJubPn4+bbrrJm+skImqxQtUKXNM+2vS4vvRmCJguF9fPUiqqrA9Qdp8raMZV1qvQWAZG1bU6n6yDyB+4HSitWbMGO3bswJYtW5Ceno5169bh008/xe+//46ff/4Zv/zyCz766CNvrpWIqMWSym9ymWC6yy7ZOJagpk5vyuCUVNWa3mMdKOn1Is7mlZu2Q2kq5TVWgVIdAyUiR9wOlNatW4cnn3wS48aNs3lt/Pjx+Mtf/oKPP/7Yo8UREfmL8T0SIROAbonhUMgNP1rVCjkiggwdDkWVGuP/1gdKey4UQqur7w9a8+tFTFq+Ax/uudika7XJKNWxR4nIEbcDpSNHjuC6665z+PrUqVPx+++/u3t6IiK/kpYSgS8WDsfbcwZaPC9NBS+oMARI5r1B5TVaHM0uNT3eefYqAOC4cR5TU6m0CpRqWHojcsjtQKmoqAiJiYkOX09MTERxcbHD14mIAs3ADjFoHxti8Zy0cW6hFCgZM0rSvS5S+U0URRzNNgRIeWU1TbrOcmOgpFYYfgVUsZmbyCG3AyWdTgeFwvFNc3K5HFot/89HRK1bbKgho1RoLL0VG3uUhqQa9ojbZQyUrpTVoKDCcMyV0qYNlCqMPUrSoEk2cxM55vZ4AFEUMX/+fKjV9ie6ajQatxdFRBQopIxSfenN8L839EvBvowiHMosQXWtDkcv15fgmjqjJJXe4sPVuFxcjZpm6FHS6vT4ZH8WhnaMRfek8Ca/HpG3uB0ozZs3r9Fj7rjjDndPT0QUEGKNPUqFFVJGydCjNKBdFNpGB+NycTW2ns7Hydz6vqSyGi2qa3UIVsmbZE1SM3e8cW2u3vVWWlWHe9f+hhv6pWDusA5OvWf9oct45pvjAICL/7zepesR+ZLbgdLq1au9uQ4iooAUb9ajJIqiqUcpJlSFG/qmYNX28/jmcDY0WsusTl5ZDVLjQptkTeVWpbeqWtfaJL48dBn7MoqwL6PI6UDpZG65a4skaiG8soUJERHZZ8ooVWpQrtFCa5yRFB2iwo0DUgAAW09dRXpWCQBAZmzyvtKE5bcKjSGrJQVKNXV6l2Y3BSnrf3WYjztoSHhQ020hQ9SUGCgRETWh2ND6jFKJcSp3kFKGYJUcPZIi0D0xHLU6PUqr6yCXCejXLgpA0/YpVWoMpTYpUAJgk9FqiFJW/6vDfLxBQ8LM9tqrdeFaRL7GQImIqAnFmuYoaVBkbOSOCVGZXp/RP8X0926J4egQYxgv0JSBkjQeQJrxBLjWp2R+7DEnA6UQs36r4irnslDUNHR6EWfyyn22KbO/YaBERNSE4ow9SmU1WuQbg58o80CpX32g1KdNBBIjDNue5JU13Z3DFTWGzFZEkBIq4ywlVwKlGrNjze/Wa4jWrLTnbLmOmsbj649g8vIdeGfHBV8vxS8wUCIiakIRQUoojI1H565WADA0ckvaxYRgcKphQ91r2kebAqWm7FGSSm/hQQoEKw2ZnmoXGrrNgypnS291Zlu1MFDyrS8PXgYAvLHlrI9X4h88DpTmz5+PHTt2eGMtfmPFihVIS0vD4MGDfb0UImrhZDLBFBidyzcESlEhSotjXru1P/56QxpuHtjWFCjluxEoHcsuNY0haIg0HiBMrTCVxKprne8bMg+Uskuqneo5qtPVZ5QKW0CgVKvVu3y3X6Bh4c05HgdK5eXlmDx5Mrp27YoXX3wR2dnZ3lhXi7Zo0SKcOHECBw4c8PVSiMgPSH1KUqBknlECDFmlBdd2hFIuQ1Kk4VhXM0pHL5di+pu78NBnhxs8Tq8X6wMl84ySK6U3q0neV50IzsybxYtbQKA07KUtSHtmo0UZkcgejwOl9evXIzs7Gw888AC++OILpKamYurUqfjyyy9RV1fX+AmIiAKc1KdUn1FSOTw2Iby+R8m62VYUReSX1ViUsSQ7z12FKALpWSUNNulWmmVRwtQKBLkTKNXZznyyPcbyfOZr9nVGSRRFU/kvo6DSp2vxJfZyO8crPUqxsbFYvHgx0tPTsX//fnTp0gVz585FSkoKlixZgrNnWQclotZLGhFQZczExFiV3swlRBgySrVaPUqqLP+x+cPRKxjy4hYMfXELnv3fcZTV1L8uzWGq0GhN26XYI/UnKWQC1AqZafq3uz1KgG2Z8NdzBej5zE94b2d9s3Cd1rxHybdbXJmXAWXS7sREDni1mTs3NxebNm3Cpk2bIJfLMW3aNBw/fhxpaWlYvny5Ny9FROQ3YsMs98SMDnWcUVIr5KbSnHX57fClYgCGZug1v17EF78ZmnJFUcThSyWm4xrKkkjDJsOCFBAEwa3Sm/Wx1nfoPfz57xBF4IXvT5qea0nN3Fp9/VpkrThOEtml5BSPA6W6ujqsX78eN9xwAzp06IAvvvgCS5YsQW5uLj788ENs2rQJa9euxfPPP++N9RIR+R1pY1xJdAOlNwBmIwIsA6XSassM05XSagCGhuqr5fXBysUGAiVp+xJpAGSwG83cUlktwjhtO7/ccp1aO1O+a3UtZzyAeUZJsJNROnCxCMNf2oKNx694/dqiKKKkhcyRYunNOR4HSsnJybjnnnvQoUMH7N+/H7/99hsWLlyI8PD63aGnTJmCqKgoTy9FROSX4qwzSo0ESknG8pt1oFRWbQhy2kQFA6jv9ZHKbpKMQseBklR6MwVKbvUoGY7tEBtqXKdlRslej5R5Rqm61rcN1FqztdirvN3x/n7kltbgvrUHGz3XnvOFeP3nM9A5uQXMM98cR//nN2PrqXyn1+tNa/dc9Ml1/ZnHm+8sX74ct9xyC4KCghweEx0djYyMDE8vRUTkl+KsM0qhjnuUADgcOilllDrFhyK7pNqUmZHKbmqFDBqtvsGMkqn0ZhUoObr7SxRFvLvzAtpGh2Ban2QA9UFV+9gQHM0utQnodI0EStbN4M3NPONlL6hzJWi8/d29AAxN+H8c2r7R49fuzQQAvLLpNMb1SHD6Ot7y12+ON/s1/Z3HGaW5c+c2GCQREbV2saGuZZQcDZ2UAqVUYyanyJRRMvQuTe6VBKDhHiVT6S3IsvTmaKbQ8ZwyvPjDKdz/8SH8/bsTAOozQqmxhu1W8q0COnvZFYuMko9vyTdfi50bCN3i7OBNibwFNEex8uYcjzNKDz/8sN3nBUFAUFAQunTpgpkzZyImJsbTSxER+SXzHiWVQmax75k97Y37vZ03jhOQSHe5dYwzBEqFFbXQ6UUcyykDANzYPwXf/p6DzMIqiKJot//GfNgkgPrxAA56lC6alfHe35WB+SNSTRkhU+nNqkdJb69HSVv/nK9nF2nNepScLZk1xtW+I95t5z88DpTS09Nx6NAh6HQ6dO/eHaIo4uzZs5DL5ejRowfeeustPPLII9i1axfS0tK8sWYiIr9inlGKDlHaDWDM9Ug29HiezC2zCHikjFLH+PqMUmGFBrVaPWQCMLJLHOQyAdV1OuSVaZAUaZvtr7QKlEyTuR0EL5eLqy0eF1Ro6nuUjAFdSVUdaup0pqCr8dJb/bVqtXrTfnPNxfyuN72XOpqtRzk0pgUklLwmt7QaieFBkAXSF2XG40/nzJkzMXHiROTk5ODgwYM4dOgQsrOzMWnSJNx+++3Izs7G6NGjsWTJEm+sl4jI7wSr5Ag1BiSNld0AoEtCGBQyAWU1WuSWGrI15hO1OxozOdV1OlOZLT5cjSClHG2jDY3ejspv5VaBUmM9SpeLqyzfX6M1BVVJkUFQyQ2/RgrMpnPr7SSnzLc5qTH+/cejuej5zE/4Or15d3Qw71HyVkbJ+o7ExrSE0ps3am8/HM3F8Jd+wcOfH/b8ZEY6vYgjl0ssmu59yeNA6eWXX8bf//53REREmJ6LiIjAs88+i2XLliEkJATPPPMMDh5s/O4BIqJAJc1SciZQUivk6BwfBsCQVQIMAYqU/EiJCjYFKCeMrycZ+5qk/qWLDu58q7DqUQpqpEcp2yqjZB4oBavkCFIa1mHeoG2eUZICJPOMUq1WD51exP99fAg6vdjotiveZlF681pGqXWW3qSNdb8+nOO1cy776RRmvLkbz/yvZTSeexwolZaWIj/f9jbHq1evoqzM8H/gqKgo1Na2jLkRRES+IPUpWe/z5khPs/IbUJ+xCFbKoVLITOc5YexPkhrApf4lR3e+XbhqeD7ZWJarHw9g/1/vUulNKo8VVGhMAVuwUg618f1SQCSKokWWRgrArLdd0Wh916dkvhZ7/VTuMJ8T5YxACZSaomz69g7DRPdP9mV5/dzu8Erp7a677sKGDRtw+fJlZGdnY8OGDViwYAFuvPFGAMD+/fvRrVs3Ty9FROS3pD6lqAa2LzHXM9mQpT95pRxAfSN3RLAhE2QKlHItAyXpTjR7pTe9XjTdndW3bRSA+h4l641uAUPQIwVK0nrMB1sGKeWmzJYU+JhvfgvUb9tiHUj4ckRAU5TeXI17ZM3blmWXNyZzK1pCCbGJedzM/fbbb2PJkiX4wx/+AK3W8C8HhUKBefPmmbYt6dGjB9577z1PL0VE5LcSjUMkrYdPOtJDCpSsMkqRwYZAS8pQnc0z3BknNW6nxtmW3r79PQfv78rAA+O6oEKjRbBSjq4JhtJeQwMniyprTc/3SAzH75dKTIGSQiZAKZdBbSy9SRklafyARAqUrDNKvrzzzWI8gI/GU7eEjJI3vnRFS4j4mphHgVJdXR2mT5+Ot99+G8uXL8eFCxcgiiI6d+6MsLAw03H9+/f3dJ1ERH7tzpGpEATgtsHtnDpeKr1dLKhEda3ONlAyZpRqjb/0rUtvmYVV0OtFyGQC3tuVgd8vleDx9UcAAH3aREJhzARJd6oVVdZCFA0N4+FBhmtI2aTECDVijIGZtF2JFGDVZ5QM65AaziWOSm++nKVk3qNkr/HcWWt21w9SdjXs8UUzt7fKjOYUct8HfE3No1BQqVTi2LFjEAQBYWFh6Nu3L/r162cRJBEREdAlIRwv3NgHKcbtRxoTH6ZGbKgKehE4nVeOMmOgFGEMYmKshlhKzdxtooKhlAvQaPXILatBnU5vykpJW570axdpel+PpHAEK+XILqnGok8Ood9zm7Bq+3kA9YFS2+gQhJv2dTNklKQmcOsepQpHGSWt7zJKtVo9Pj9wyXQHn/l4AE8ySs9+e8Lt98p9kFGy/lq9ETZJAXcg8/grvOOOO/D+++97Yy1ERGQkCIKpL+hUbpnD0pskKdIQOCnkMrQzzje6WFCJc/kVFrfmA0C/dlGmv0eHqrDg2o4AgB+OXoFeBD47cAlA/WiANlHBpiyTVHqTMkpqq4xSucbyNvnqFtCj9MHuDDy2/ggmvrYdgOWmuE2RZXFGY7O0vKFOp8f/fs8xZQG91Y9lTskepcbV1tbivffew+bNmzFo0CCEhoZavP7aa695eokWZ8WKFVixYgV0Ot9OlyWiwNYzORy7zhXgZG6Z6Xb+iGApo2QZKEmlN8AwZ+nC1UpkFFQiu8Ty9n4A6Gds5JbcO6YTPt6XiWLj0ESpzyjH+N420cGIsM4oGXuTTD1Kxp+H1j1KlValN4VMgFYvQuNkRulsXjnSL5XgloFt3Q4ufj1fCKA+OHN2MrcrMYCrS2uORMw7Oy7g5Y2n0T0xHBuXjPbacE1zgTpk0pzHgdKxY8dwzTXXAADOnDlj8VpzRMy+sGjRIixatAhlZWWIjIxs/A1ERG7olmjoUzqbX4FOxmnc9gKlEJXcNEASMGvoLqg09TBN65OEbaevIikyyDSUUhIRpMR78wZjz/kCvLLpDAoqNCitqkOOcdhlSmSQqfQmselRqmuk9GZcR3iQAsVVdU73KE1avgOAofl59sC2Tr3Hmsqqj6ah0pv5JrneblQ2P3dz9Ch9/pshM3g6z3DnpHVQaG9DYFcF5m95Sx4HSlu3bvXGOoiIyEon49DJjIJK08BK62ZuwNCfZP4PU/M736S+pCm9kvDE1J4IVsnt/iN2YIdoDOwQjY/3ZSG3tAbnrpYjt9SQUUqOrC+9SaQm8PqMkv1mblPpTSsFSkoUV9W5XHrbcjLP5UDpmW+OobiqzmLWj14vNlh6Mx9v4EqcJDgRMpgHKs1x11uZ1bTwpii9BWg+xILHgRIRETWNTsaAJ7e0BnnG7I5UAjPPKJmX3YD6LU7OX600BTu920Saepca0iUhDLmlNTibV4HcEsM1k6OCbDIgwSoHGSWNbelNrxdNs4ukOVCuNnPnm81vcoZWp8dHezIBAH3b1mf+ez+70ZTlAgwzlTILK9E+JgSCIFi8pnShPuZMwGAeoDVHoGQdENtklJp8BYHBK3nFnTt3Ys6cORg+fDiysw179qxduxa7du3yxumJiFql6FCVaUDlsRzDoMj6jFL9XW/Wm9+mxtUPnayp0yNUJTcFT43pYpyvdDynzJSNSrGXUVIYM0rG/5UySpeKLPeGq67Voc6s1BWuNpyn0sGWKY7kldW4dHyNWWbIPFyoshqs+eIPJzHm5W34ZL9hCnR5TX0Wxp1G71qtHsdzSu2WtWrNRiQ0R+ktyGpqtq9mRvk7jwOl9evXY8qUKQgODkZ6ejo0GkPUX15ejhdffNHjBRIRtWbSXCTpF7wUKEUEK0xTka0zSimRwRblpuGd45xuupUCpd3nCgAYmrajQpS2PUpSRkkhZZQM65MCOmlCeFWtziKTIp2nqLJ+WytnlpZf5lpGqdosIGqoX1badPgf358EYNmMXudioCSKIma8uQvXv7ELu4zfP3PmGZ3mqFhJ5VGJo5lRBzOL8HV6Nj47kIWP9lxs+oX5GY8DpRdeeAGrVq3Cu+++C6Wy/l8cI0aMwKFDhzw9PRFRqyYFShKpmVsQBEQby29JEZYzlWQyAXeOTEXP5Aj8bXoa/nP7AKev18XYF3XBuAVKSmSwYVaeSmFRXgpVSxklY6Ck00Oj1eG0ccuVIR1jABgGTprPUJIyUyVV9ZkbEY03Ftfq9C5leMxLe1onpkpKX4d56bBWq8f1b+zEuv2N7zmm04vIKa3BKePXL82usj5G0tgdaJeKqkzfS1flllZj8vLtpv+GEuvvg7SEm1fuwUOfHcbj64/imW+Om8YJOMOZ3ix/53GgdPr0aYwePdrm+YiICJSUlHh6eiKiVq2TVaAkZZSA+u1QrEtvAPDE1J74cfEo3Dmyoyn744zuSeEWGZ7kKMO5ZTJDsCTpnWLo+6nPKOlxNq8CdToRkcFK0x17hoyS4Re0XCYgzBhgFZpllETRsn9HYj3N25U+JfNAybrcZo9UQrS+a+94Thme+OqozfHWgZ1eFFFlFmRZZ3Os36NtJOgbtWwrpry+A4UVjr/mjIJKfHM422YtL288jTPGrW0kT204ijve39/gNSXW3wPJ+7syXM44VTfyvS+qrMUf3tmD9Qcvu3Te5uRxoJScnIxz587ZPL9r1y506tTJ09MTEbVqHeMsdzqIMAuU/m9sZ0ztnYRRXeO9dr2oEBWGdow1PU6OrB8lIN3hBtRnjKQAQ6PVmzbc7dMmEiHGoKpSozP15ijlgimAKDYLlAzvt/2Fat3wbW8mlCPmd9WVVtU1cKSB9LX9eOyKU+e3DnR0Zg3rgOWsJon5WxrKKJkHPpeKHX/N417ZhsWfHsa7Oy9g1lu78e3vOQBsNyYGgI/3ZdlkmByxF8RVaLT4+3cn8Mw3x1FSVWvnXQYlVbX48WguNFodntxwFD2f+Qkncmyza5Llm89g74UiPPLF706tzRc8DpTuu+8+LF68GPv27YMgCMjJycHHH3+MpUuX4v777/fGGomIWi3z0ptcJiDULDs0o18KVs4ZiFC1d29gvr5vsunvKWbZqoKK+l+Q0rqkjFKtVo/0rGIAQK82EaaBlBptfY+SYRPd+r3lzNn75W49aynHhUDJ/L0l1Y0HSiq5DJeKqrD+kHOZDetsl1YvNjrI0ryZuqFb9c2bvp3p33rxh1M4lFWCP69LB+D59ij2gjzz9V4qcvzf4Y/v7sP/fXwIr246g0/2GUqWK7bZJlMkzvy38TWP/9/12GOPobS0FOPGjUNNTQ1Gjx4NtVqNpUuX4oEHHvDGGomIWi3pDjbAUHZrjkG+U3sn4emvjwEA5HaGCQlCfYO01NtTU6fDvgzDBOxRXeJNd4/V1NWX3lRymWlQZXFV44GSxmrWkiuBknk2ypn5QdV1OhQ0UOayVqe1Kr3pRYu7++xlZcx7rHQNtE2ZbznjzhgBT++os/f9+u5IjunvZWZ3Blov74SxN+t/h+uPb2g15tmzn0/kmbKSLYlX/hnyj3/8A0899RROnDgBvV6PtLQ0boxLROQFISoFUiKDkFNaY5qh1NRiw9S4rlcSNp24gut6J9U/H6pCYWUtbh3YzvSclFHac6EQRZW1CA9SYGinGOw6a7jrq6ZOb/rFr5TLTJkmm4ySnblK1qU3KVA6faUcIkT0SIpw+DU4O/lbUlpV12CAIYqiRZBaayejpLMIhGwjIfNqm73X95wvRLKdKeiu8jRQstf8/tSGY6a/m38djuI48+d/OJqLK6U1dnvpzN390W8urbO5eO3/dSEhIRg0aJC3TkdEREYd40ORU1pj0cjd1F7/Q3+UVdchwWz0wNoFQ/HdkRw8ML6L6TkpoyQFPhN6JBhLbIbnq+vMepQU9T1K1hkkexkl6+nd2SU1qNPpMXvVryiv0eKnh0Y5DJZcHWhZrtE2OC1co9VbNGhbl970omjxnN2MknnpzerlEzlluP3dvQCAPU+Mb/A8DRFF0TQ2whn2vk/mAZ91gAgYSoh1On2DAznN36EXgQmvbsPx56+zs16nl+ozXgmUtmzZgi1btiA/Px96q0j0gw8+8MYliIharY5xodh9rtCikbupBSnlNndupaVEIC3FMjBRWQ01HNE5zvR+wFh6s5NRsmY3UNLaZpSqanWmWUcv/3Qa788fbPd8rgZKAHDr23scvlah0TYYKNlmlBruUbIedXDMrORkXnrTNlSjs0OnF13aqPbNX2z7h6TgbM/5Qiz870E8P7OXxevzPtiPhHA1dj0+3uF4AOvgqtLO3W8rt53H90dznV6rr3jczP3cc89h8uTJ2LJlCwoKClBcXGzxh4iIPNPdeKt9QnjDpQtfkO56k0hbqwSbAiW9qZlbJZeZJnpbs1d6k24tl0pJOaXVFnfHZVlNAbf3XnekJUfYlDkrrbZmsQ6UDCMOGs4oiQ00c5tnm8wDJXtjExqiF11r5n5zq22gJK1t/ur9KK2uw+JPD9sck1+uwekr5RaBjvndbc4s4V8/nXJ6nb7kcUZp1apVWLNmDebOneuN9RARkZWbB7aFRqvHlF5JjR/czNRWGSUp62UqsZk1c5uX5KzZL70Zgp1OcaE4m1+Bkqo6i0GV9t5jem8DrzVGqZDZZMrKa6wDJdsAxrz53F5GSW/RoySiQqNFqHGTYvOjNRaBkmtfh14UPe5Rkq7ZWIhWZ1VBunNN/ZymQNos1+OMUm1tLUaMGOGNtRARkR0hKgXuHtXJqU1tm5t1oCQ1IkslthqtzpQFUilkNhkoif3Sm+G5uDC16bwZZrOAGiqv2cso/evmPpg1oI3D90hUcgEKq7v9KjVapGcVm/acsxfAaCxKZg33KB3PKUXvv23EI58b5geZ9+pYnMeJqeLmQYleFD0OUqQgr7HT1Fn9N8sz22bGXknueE7Lu6PNGR4HSnfffTc++eQTb6yFiIj8jHWGyJRRMgZEdToRZcZsTIhKbhNYSewFPdJzQUoZ2kQZBl+aB0oNZpTsnC8mVO1Un5dSLoNCbvmL/vH1R3DTW7/i2n/9AsBRoGQ+ksD2dfMsk9Sz81W6YSN5Ee6X3kSrTJWn24pI12ws4LJeW4jZjC97753r5GRwc3d8sB+Xix2XWJuDx6W3mpoavPPOO/j555/Rt29fi/3eAOC1117z9BJERNRCqeSWGSKpt8d82xRpCrchUHIho2QMdoJVcqREBePUlXJcdDKjZO81hVxw6o4whVxmc0fXxULDL2spOKjV2im9aRvrUXJ8TfPXzEcPOCq9Odobz8Wb5Bo8d2MBl/XawoMUpu1i7L2zqLIW1bU6l7bU2XHmKhZ9fAjfPHCt0+/xNo8DpSNHjqB///4AgGPHjjV8MBERBRTzjJJMAEKNW5eYZ46KqqRASdFAj1IDGSWFHLGhhn3tLlhllOzdvg7Yn6OkksugdJDRsjxOgFLuOEioqdPZ7UEyzwTZ71FqYNsSB+exV8IDHI8NEEXRbjbLFdKpG88oWV7HvNzpaFBmYaUGbVWulZB/v+zbkp3HgdLWrVu9sQ4iIvJDKrPMS3iQ0nRruiAIUCtk0Gj1KKowzyg5CJTszDCS5hqplYaMEmBZegMM2RfrLFV1rQ6f/2a7FYlSLoPSiYySUi6z6VEy99+9mXjh+5N21lsfKNifo9TARc2CqKra+sZxRxklR9PGdXoRdR6mlYoqNfjPlrONbiZsXXqzyAo6HETpellw4ZjOLr/Hm9zuUZo2bRpKS+ujvH/84x8oKSkxPS4sLERaWppHiyMiopbNPEMUEWz5b+8gq33d3C29BSllSIkyjEa4Wq6xOsb2fb+cyjf9vUNsffZCKRegaGBIYv1xsgYzSuZBUp82kab92DSNZJQa2krF/BXzzEytTo+P9lzEoSzLcTuOAii96PrsJWt//eY4Xt18ptHjrBvNzb9+R989RyXDhrSLCW78oCbkdqC0ceNGaDT1H9h//etfKCoqMj3WarU4ffq0Z6troVasWIG0tDQMHmx/0BkRUWthnlGSym4S6c63wkqz0ptVRkm6Db+61vL2e6A+CApWyk3N3NbslewKKw2/m7onhuOWgW1Nz9tr0gaABdd2tHhsOM65X48ymWC6Hb/xHqUGAiWzl8z3tPv1fCGe+eY4Zr31q8X7HZXkDBPCm2fcdUPN9I4yR5Ne22GzfU1jPG1O95TbgZL1f3B3okR/tWjRIpw4cQIHDhzw9VKIiHxKbTat2nqSt/UGuCEquU2PUnyYofeoXGMbKFWbMkr1pTdr9kp2ZcYd6Qe0j0KMsbcJkEpvtr/2JvRMwL//0N/0WKVouEfJnFyoH4j53s4Lpue//T3HYiNZoOHSm3n/0htm07JLzeZG5ZTWoEKjRX55jc0MI/Pz2Asem4L1dHFzjiqc1XU6XPP3zUj9y/dOX8fDsVAe83g8ABERtV7mGSXrbJG90pvKKlMTF2aY5F1RYy+jVF96SwhX2x2kaC8oKDUGShHBSsSE1t+JrXTQpB2qUpgCNsNxtne9OSKXCaZJ2NZxwwOfpFs8brD05uClXecKTH8vrqzFNX/fjCH/2IL8Mo3d43V60aIZvCnpGkiQeDML5OvhlW4HSoIg2KTW3GnSIiIi/2UeeKitMkrS43LTHCUFFHKZxS36scYApcJORqm8xhjwBCmhkMuQFGG7hYu9HqWyaq3xfQqbjJK9klqoWoHwIKXlcU6mMWSC4PQk7IYqLw0FURK9WB8EpVv1LNVfo+GSmDc1tGZvhgO+ji3cvutNFEXMnz8farXhQ1hTU4OFCxciNDQUACz6l4iIKDCZ/xILss4oWT2WBhKqFTJojQ3LpoySnUDJFPAYh0SmRAUh26x/B7CfUSqrMc8oqUzPqxT2m7TD1AqLwMiVHiW5zPlAqaFYyFEpzZx5YGIvQJSOaQmBkjc5GjXQXNwOlObNm2fxeM6cOTbH3HHHHe6enoiI/Ix1Rsl6sGCI2vArR6WQmSZTSxkl673UALOAJ0gKlIIBWGZS7PYoGd8XGaxEVIjlEGR7t/2HquUWAZRchibJKDU0R8lRc7bFMRaBkv0+JPOsU1M7mOl443tvZoF8XatyO1BavXq1N9dBRER+zjajZBUomTJKcgCGYCY21HFGSQqepH3ezPuIJDX2Mkqm0psSMSEqw/tFICZUZbckFKpSQKWoDy5qtXqLoGbJxG74+WQeIoOVFj1DgOVdb41pqKfHmVv6zYNCR1kjfTOW3r47kuvwtZO5ZV67TgMjrZoFm7mJiMgrrO9oC1I6KL2ZPR8fbuxRspdRMmvKBoBoszKapKGMUkSwAjKZgANPTcS+pyZAKZfZLRfJZILFfKeaOr3FcYvGdca3f74WCeG2gZpcsJ+lsqehHiVnhkSaZ5EcZZT2ZRSitpG73q7vm9zotVoSX48H8HgyNxEREQCbYZLW4wJC7GxvEmcqvdVZHKvTi6aRAdL+cdZlNMBRRsmyZGe+jobKX6ZzWm1RIvUryexkjuQyweG0cQD45VQeNp/Iw+PX9UBDbUhOZZS0jWeUntrQ+FZinePDGj2mJfH1fWIMlIiIyCMpkUHIKa3BtD6WmQrbQMnwWGUWWMQam7kraw3BiVwmGIIks8BJuiMtKrjxjJIoiiirsWwCN+dM/3GNVg97LUNyO7+xZYJg8fVYe/KrY7hSVoPMwirMH5Fq9xhRFB02Z5szb1yvtDOg01m+7vlxld82cxMREQHAT0tGI6ekGj2SIiyedxQomQccsWa371fWaiEXBExevsOUPQpSykyBSLSdjNLavZm4ZVA7U59QVW19NigiyPZ4Z+7UqqnT2R2mKLdzx5xc1nCgdKWsBgBw5HKpw2yWXrS/ia/tuuqDKXulSmf5OkPjKl+vlz1KRETkkYggpU2QBNjrUTL829w8XIgIVpiGUFbUaLH9zFVkl1TjeE6Z6dySqBDbjNLxnDJ8f7S+qVgaNqmUCzbXB5zbRSIxQm03oLKbUWqk9CYJVskdZrO0er2TgVL9MZtO5DV6vCO+7vlxla8zSgyUiIioSZhnlNQKmd27w1RyGcKMPUgVGq1NgGJePrPXowRY3mFlPlLA3i3q8gYar9cuGIIZ/VLwyKTudu9Qs7d+eSOlN4lKLnOYUdLpRdTUNh4oeetuNl9naFzl6+Wy9EZERE3CfFxAiNVMJYkgCAhTK1BUWYvyGq0p0JFIjdwAEG0nowTUjw8AbIdUWrtxQAo+2nMRQzvGIDkqGBN7JppeG9U1HqO6xgOwv4+Z3UBJJthsy2JPVa1tECjZd6EIO85ebfQcru7h9uz0NDz77Qmb5/u0iXTpPL7mt5O5iYiIGmI+cFIquwG2+5qFm2WUCsotd5Y3D3isB1hKSsw2jq2/483+r7cQlQI/PTS60bU7m1Fy9nd4hUbrMKN05xrnNlh//eezzl3MaHyPRJtASSET0CE2xKXz+Bo3xSUiooBkGRw57g0KM07sLq+pQ0GF5fZX4XYasgFgUIdo09+LK2txpbQG6VnFFtuXeMJeRsler4xcECwmZjtSp3PuzjZvsldlTEuJ8HnPj6uYUSIiooA0qmuc6e9FVbUOjzNllGq0uFpuGSilRNluhAsAtw5qh5uuaYOnNhxDcVUd5n2wH6fzyjG+RwIAzwMl+xkl2+PkMsGp2UxAfbarIWqFzGu9SHYzYPB9c7SrmFEiIqKAFBWiwteLRqJtdDAenNDV4XFS5qmyVmeTURrYPtri8Wf3DsPiCV0x65o2pp6l0upanM4rBwD8ciofgP3RAK6w3n4FsN8ILpMJqHNinzYANv1Xdq+rtF9edIe9gEgQBP9r5ubASSIiClT920Vh1+PjLZ4TYRlYhBpLb5UarU2gdE0Hy0BpaKdYDO0UC6D+LrjiKtsAJCLYs19vL83qg3s++g1/Hl8f4NkbDyAXBKdmMwFAdW3jmaIwtcI04sBT9gIlmWB/wnhL5utxBgyUiIjIp0KNTdqVtbaltzg7G+FKpIxSiZ2ynqcZpa6J4dj26DiL5xyV3pwNlJy5ay02TIXskmqnztcYe6U3mSDYDfhaMl8Hdiy9ERGRT0kZpcKKWlQa5wmN6hqHFX+8psH3SYFSQYWdQMnDHiV7HM1gMg+UPrxriM3rkca1ONPMHezV0pvtc4IApwZktiS+Duz867tFRER+z7r3OVRtCA4uF1cBMEzV/uiuIY3uch8VonTYvxKu9n7BxF5GSRRFi7vehnaMsTlG2s/O3ga+nkiMqM+2DTeWI83Zy8S0iwlxuw8qrAm+p87wdaWQgRIRETWrx67rAQC4Y3gHAPUZpUtFhpJTdIjKqVvCg5RypCXbbp0CwKlp2a6y1/OjFwGdvj5TpLQTTUkDKbMKq7y6HvPsU992kRiSahmk2cvEPH19mtsZJXulvNaAgRIRETWrMd3icfiZSXhuRi8AQKjxrjepNycm1P4EbnuuNRtBYM6Zadmushco6EUR43sYpnsnRwbZHPPolO6mwORodmmj13Cu28nAPDMkFwSbuUnWa0mJDEJMqMrtnh8FAyUiIqLmEWWWNQq1Kuk42qrEHnulLgBQNkFGyV6goBeBhyZ2xb9u7oMN94+0eG3usA64f2xnu1kme9rHuDYxW20WKClkgk1gZJ1QcrLn3CFfZZQ8XLbHGCgREZFPhVptTeJKRqldtP3gQin3/i91+5kYEUFKOW4b3B5JkZbDMaNDDcGgs2XAVXMGurSeYGX9eePC1TalQevSm7ODMR2prnOvx2r+iFSPruvhsj3GQImIiHwqxDqjFOr8HWuJkfYndzfFnV32en70DdzIJm3b4kxG6aVZfZCWYr/fyhHzHqWBHaJtMj7Wjys1WpfOb63GzUDp2Rm98Pcbe3t0bV9ioERERD4VprbKKLlQenN0d5uz5S5X2MsoWQ/PNCeNDXAmo+RKuVFi3vDeNirEJpCzbog3L719ft9wl6/naHTB9H4pjb7X1fzenGHtTX9v6HvcHBgouWHFihVIS0vD4MGDfb0UIiK/Z755LmAoWTnL0d1xTXHXm70epYbKQlJg4kxjeXSI63OfzPeOiwhW2A3kbh9SH3CsnFM/l2qIg96uhlzf135AFOnEFHRXRyG9cGMf0999XXrjZG43LFq0CIsWLUJZWRkiIyN9vRwiIr9m3cztSo+SI02RUbJ/15vj46XSmzNBm+lrdiEouLZrHH7LLEbHuFAIDiZu/31mL/xpaHukJUd4POG6Z3K43eed2WLE19uQeIKBEhER+ZR1M7c7ZShrTTEewN4cpYbKQnpTj5Ll+wZ1iMbl4mpcKasxPRflxtfcLjoEh/46yTSw097gcIVcht5tvPMPekehjjPxlyfDtXnXGxERtWoKucwi6+JqRum1W/vZBCMtofQ2vHOs3bWM6hqPvU9OQI+k+gxNlJOlt87xoaa/BynliAlVQa0wBkpNvdWHg/M7MxzUnZVJwW4fLwV67mKgREREPlerrb99rEtCmEvvnXVNWxx7bgq6Jda/r7maue3dcr/nifH48K4hGNc9AQCgkltmzKTAqbym/i40e+t9ZFI3i8e9UiLw1f/Vz2qyvrPPm3OOUuzcTejJ6d2J4dKfmYTfnp7olVKsJ1h6IyKiFiNMrXBrLzIpqyJpijlKdscD2MkoJUcGIzkyuH4tCsv3SQGORtvwJrnmp35iag/cMqgd1Gazk8z/7mh97rIXFDrqM3Lmsq70KN0+pB0AQ++adf+aL/h+BUREREbD7Gzu6izz5E5TlN7sZWxEJ27JCrIK4jobM2Z1uoYDJfNs1X1jOtu8xzo49LRZ29zl4mqb5xwFRN4u+f1tei+vns9TLL0REZHPPTi+C5IigvD8TO/8klTa62z2kN1AyYn3mWfIuiaEYbRxfzptI4GSvcyaedbIOhj0ZkbJHkdxWEGFptH3OjsLqU1UsFsZxabEjBIREfncw5O74+HJ3b12Pm9mVyTuZpTMtxp5YHwXU/NzXSObr80d1gGbT+Rhclqi6Tnzr6tjXKjF8U0QG1pwVD7T1DUc8AG+n4XkCQZKREQUEJr6d7Hd8QBOXNQ8Q2I+XNNeRsk88xKqVmD9/42wOWb/UxNQq9UjMtjxnXJPX9+z8YW5qKlvqgOcCzybG0tvREQUEJr6l6z9gZNO9CiZBUrmM6MaSSg5lBAehLZ2NgM2X8ofzCZyOzKgfZRL13U0BsCZ74GzX2rLC5MYKBERETmlvKbO5rkeSY1vZGseKAWrmqf/xpl+pdXzXduGy9EZzQO+ZAebFLfARJHTWHojIqKA0NS/i+t09Vf49oFrsflkHv5vbOdG3xdk1qPU2O3ungQU5u91ZqaSq9PAHfVAmWeUokJUyC2tsX+gE1piQMVAiYiIyAkTeiZgSq9EDOkYiz5tI9GnrXMToy0ySk14R5d5wGJvirinHDVz68xSSqmxITiZW2ZzjLN3vSU6yEj5EktvRERETlDKZXh77iAsuLajS+8zv40/pJlKb01x15+jap5eFPHJPUNxfd9kPD+zt1vnXnfPMIzpFo83/tDf/QU2EWaUiIgoMLTAso0189LbnGHt8d+9WZg3vINXzu2NL/+RSd3w6/lCTOmViGe/PWHxmlZn/wqiCIzoHIcRneMcr62RxQ3vHGvaG6+lYaBERETUhMyDBPP92f42vRduGtAGfdtGef06zooIUqDMbM+5EV1i8ecJXbHz7FWbY3edK7B7Dp27t+/5CZbeiIgoILTUX9d920YiLTkCk9MSLW6xV8plGNghpkk28HXWjsfG4YcHR5keS8GWvX6kYZ1i7J5D58XxAC0RM0pERERNSCmX4fsHr3U4h8icJwGFsw3T5qJCVIgIqh9cKSWH7C31xgFt8Pj6ozbPO9U43hJvZ3MSM0pERERNzJkgyWNuxiLmjd/S0E57q7XehFfS2MgDoOGl7Xh0XKPv9yUGSkREFBBa4vYXzckbX73pHC7EdWHOBEoOFvfCjb3RPtZ2ynhLwkCJiIgCwtjuCQCAuDDXBimSYYNduUxAnzaG2VD29rWzNsm4We9dI10blwAAE3ok4Kv7R+CPTmy14mvsUSIiooDw2HXd0Sk+FBN6Jvp6KW5LinB/4KInGbXNS0ajTieatlixDpNm9EsBAISrFSjXaNGvbSTenjMQJdV1iAltPDC1XtuquQN92sTuCgZKREQUEEJUCtwxPNXXy/DI32akQaPVY84w1zMtntylr5DLYN6CZN1TJW3D8sX/DcfqXRfx4MSukMkEu0HSy7P74tEvjzR4PX8JkgAGSkRERC1GQngQ3ps3yNfLcDiFu0dSBP41u2+D771lUDsczynDml8vmp7z5+4x/wnpiIiIyCFvBiPe3gHFn/vsGSgREREFAO/e9WcZKflzoOMpBkpERERkwbr05mmcZL4xsL/x35UTERGRSdPlkwC5iwMzEyLUFo9vHNDGwxX5Dpu5iYiIAoEXIyXrOUoyF5uW7hrZEefyKjC5l2FUQ5hagZFdYrH7XKHX1thcGCgRERFRg1xt7g5SyvHabf0tntPrvbee5sTSGxERUQDonBDmtXNZJ6ecmdTdGL2fdoQzo0RERBQA7h/bGRqtDpPTPJ9MrrOaXumNcQF+GicxUCIiIgoEQUo5npja0yvnsh41YD2p2x3+mlFi6Y2IiIgsWG+H0ppLbwyU3LBixQqkpaVh8ODBvl4KERGR11lnlLxRevNkLzpfYqDkhkWLFuHEiRM4cOCAr5dCRETkdTYZJS9ESt6dHN58GCgRERGRBdseJc/PyYwSERERBYSm6FESvTo7vPkwUCIiIiIL1o3Xrm5hYvecHDhJREREgcA6ULp1UDuvn9NfcI4SERERWTCPaY49NwVh6tYbLjCjRERERBau6RANAEiJDPJakMSMEhEREQWEyGAljj47GUFKudfO6a93vTFQIiIiIhvhQUqvns9fM0osvREREVGT89M4iYESERERNT1mlIiIiIgcYKBERERE5AAHThIRERE5wE1xiYiIiBzw1/EADJSIiIioybFHiYiIiMgBZpSIiIiIHGCPEhEREZEDLL0REREROTBnWAcAwKiucT5eiWu41xsRERE1ucUTumJ4p1j0bx/l66W4hIESERERNTmFXIYRXfwrmwSw9EZERETkEAMlIiIiIgcYKBERERE5wECJiIiIyAEGSkREREQOMFAiIiIicoCBEhEREZEDDJSIiIiIHGCgREREROQAAyUiIiIiBxgoERERETnAQImIiIjIAQZKRERERA4ofL0AfyaKIgCgrKzMxyshIiIiZ0m/t6Xf4w1hoOSB8vJyAEC7du18vBIiIiJyVXl5OSIjIxs8RhCdCafILr1ej5ycHISHh0MQBK+cc/DgwThw4IBXzuWt87lzDlfe4+yxjR3X0OtlZWVo164dLl26hIiICKfW1VJ5+zPiq2t6ek5/+Vw2dAw/ly3vur74XLr6Pk8+c84eE+ifTVEUUV5ejpSUFMhkDXchMaPkAZlMhrZt23r1nHK53KsfSm+cz51zuPIeZ49t7DhnzhMREeH3/6f39mfEV9f09Jz+8rl05hh+LlvOdX3xuXT1fd74zDl7TCB/NhvLJEnYzN3CLFq0qMWdz51zuPIeZ49t7Dhvf+9aKl98nU1xTU/P6S+fS1ev66989TW2tJ+Z7r7f259Nfi7refp1svRGrUJZWRkiIyNRWlrq9/86osDBzyW1VPxs1mNGiVoFtVqNv/3tb1Cr1b5eCpEJP5fUUvGzWY8ZJSIiIiIHmFEiIiIicoCBEhEREZEDDJSIiIiIHGCgREREROQAAyUiIiIiBxgoEVm56aabEB0djdmzZ/t6KdSKfffdd+jevTu6du2K9957z9fLITJpbT8jOR6AyMrWrVtRUVGBDz/8EF9++aWvl0OtkFarRVpaGrZu3YqIiAhcc8012LdvH2JiYny9NKJW9zOSGSUiK+PGjUN4eLivl0Gt2P79+9GrVy+0adMG4eHhmDZtGjZu3OjrZREBaH0/IxkokV/ZsWMHpk+fjpSUFAiCgK+//trmmLfeegsdO3ZEUFAQBg4ciJ07dzb/QqlV8/RzmpOTgzZt2pget23bFtnZ2c2xdApw/BnqOgZK5FcqKyvRr18/vPnmm3Zf/+yzz/DQQw/hqaeeQnp6OkaNGoWpU6ciKyvLdMzAgQPRu3dvmz85OTnN9WVQgPP0c2qvI0IQhCZdM7UO3vgZ2uqIRH4KgLhhwwaL54YMGSIuXLjQ4rkePXqIf/nLX1w699atW8Wbb77Z0yUSufU53b17t3jjjTeaXnvwwQfFjz/+uMnXSq2LJz9DW9PPSGaUKGDU1tbi4MGDmDx5ssXzkydPxq+//uqjVRFZcuZzOmTIEBw7dgzZ2dkoLy/HDz/8gClTpvhiudSK8GeofQpfL4DIWwoKCqDT6ZCYmGjxfGJiIq5cueL0eaZMmYJDhw6hsrISbdu2xYYNGzB48GBvL5daKWc+pwqFAq+++irGjRsHvV6Pxx57DLGxsb5YLrUizv4MbW0/IxkoUcCx7uUQRdGl/g7eXUTNobHP6YwZMzBjxozmXhZRo5/N1vYzkqU3ChhxcXGQy+U22aP8/HybfyER+Qo/p9RS8bNpHwMlChgqlQoDBw7E5s2bLZ7fvHkzRowY4aNVEVni55RaKn427WPpjfxKRUUFzp07Z3qckZGBw4cPIyYmBu3bt8fDDz+MuXPnYtCgQRg+fDjeeecdZGVlYeHChT5cNbU2/JxSS8XPpht8fNcdkUu2bt0qArD5M2/ePNMxK1asEDt06CCqVCrxmmuuEbdv3+67BVOrxM8ptVT8bLqOe70REREROcAeJSIiIiIHGCgREREROcBAiYiIiMgBBkpEREREDjBQIiIiInKAgRIRERGRAwyUiIiIiBxgoERERETkAAMlIiIiIgcYKBER+dD8+fMhCAIEQcDXX3/t1XNv27bNdO4bb7zRq+cmai0YKBGRV5n/4jf/Y74RJ1m67rrrkJubi6lTp5qecxQ4zZ8/3+mgZ8SIEcjNzcWtt97qpZUStT4KXy+AiALPddddh9WrV1s8Fx8fb3NcbW0tVCpVcy2rxVKr1UhKSvL6eVUqFZKSkhAcHAyNRuP18xO1BswoEZHXSb/4zf/I5XKMHTsWDzzwAB5++GHExcVh0qRJAIATJ05g2rRpCAsLQ2JiIubOnYuCggLT+SorK3HHHXcgLCwMycnJePXVVzF27Fg89NBDpmPsZWCioqKwZs0a0+Ps7GzcdtttiI6ORmxsLGbOnImLFy+aXpeyNa+88gqSk5MRGxuLRYsWoa6uznSMRqPBY489hnbt2kGtVqNr1654//33IYoiunTpgldeecViDceOHYNMJsP58+c9/8ZauXjxot3s3dixY71+LaLWioESETWrDz/8EAqFArt378bbb7+N3NxcjBkzBv3798dvv/2Gn376CXl5eRblokcffRRbt27Fhg0bsGnTJmzbtg0HDx506bpVVVUYN24cwsLCsGPHDuzatQthYWG47rrrUFtbazpu69atOH/+PLZu3YoPP/wQa9assQi27rjjDnz66ad44403cPLkSaxatQphYWEQBAF33XWXTSbtgw8+wKhRo9C5c2f3vmENaNeuHXJzc01/0tPTERsbi9GjR3v9WkStlkhE5EXz5s0T5XK5GBoaavoze/ZsURRFccyYMWL//v0tjv/rX/8qTp482eK5S5cuiQDE06dPi+Xl5aJKpRI//fRT0+uFhYVicHCwuHjxYtNzAMQNGzZYnCcyMlJcvXq1KIqi+P7774vdu3cX9Xq96XWNRiMGBweLGzduNK29Q4cOolarNR1zyy23iLfddpsoiqJ4+vRpEYC4efNmu197Tk6OKJfLxX379omiKIq1tbVifHy8uGbNmga/XzNnzrR5HoAYFBRk8X0MDQ0VFQqF3eOrq6vFoUOHijfccIOo0+mcugYRNY49SkTkdePGjcPKlStNj0NDQ01/HzRokMWxBw8exNatWxEWFmZznvPnz6O6uhq1tbUYPny46fmYmBh0797dpTUdPHgQ586dQ3h4uMXzNTU1FmWxXr16QS6Xmx4nJyfj6NGjAIDDhw9DLpdjzJgxdq+RnJyM66+/Hh988AGGDBmC7777DjU1NbjllltcWqtk+fLlmDhxosVzjz/+OHQ6nc2xCxYsQHl5OTZv3gyZjMUCIm9hoEREXhcaGoouXbo4fM2cXq/H9OnT8a9//cvm2OTkZJw9e9apawqCAFEULZ4z7y3S6/UYOHAgPv74Y5v3mjeaK5VKm/Pq9XoAQHBwcKPruPvuuzF37lwsX74cq1evxm233YaQkBCnvgZrSUlJNt/H8PBwlJSUWDz3wgsv4KeffsL+/fttAkEi8gwDJSLyqWuuuQbr169HamoqFArbH0ldunSBUqnE3r170b59ewBAcXExzpw5Y5HZiY+PR25urunx2bNnUVVVZXGdzz77DAkJCYiIiHBrrX369IFer8f27dttMj2SadOmITQ0FCtXrsSPP/6IHTt2uHUtZ61fvx7PP/88fvzxxybpgyJq7ZifJSKfWrRoEYqKinD77bdj//79uHDhAjZt2oS77roLOp0OYWFhWLBgAR599FFs2bIFx44dw/z5823KS+PHj8ebb76JQ4cO4bfffsPChQstskN/+tOfEBcXh5kzZ2Lnzp3IyMjA9u3bsXjxYly+fNmptaampmLevHm466678PXXXyMjIwPbtm3D559/bjpGLpdj/vz5eOKJJ9ClSxeLkqG3HTt2DHfccQcef/xx9OrVC1euXMGVK1dQVFTUZNckam0YKBGRT6WkpGD37t3Q6XSYMmUKevfujcWLFyMyMtIUDL388ssYPXo0ZsyYgYkTJ+Laa6/FwIEDLc7z6quvol27dhg9ejT++Mc/YunSpRYlr5CQEOzYsQPt27fHrFmz0LNnT9x1112orq52KcO0cuVKzJ49G/fffz969OiBe+65B5WVlRbHLFiwALW1tbjrrrs8+M407rfffkNVVRVeeOEFJCcnm/7MmjWrSa9L1JoIonVRn4jID4wdOxb9+/fH66+/7uul2Ni9ezfGjh2Ly5cvIzExscFj58+fj5KSEq9vX9Lc1yAKVMwoERF5iUajwblz5/DXv/4Vt956a6NBkuS7775DWFgYvvvuO6+uZ+fOnQgLC7PbwE5EzmEzNxGRl6xbtw4LFixA//79sXbtWqfes2zZMjz99NMADHf5edOgQYNw+PBhALA7foGIGsfSGxEREZEDLL0REREROcBAiYiIiMgBBkpEREREDjBQIiIiInKAgRIRERGRAwyUiIiIiBxgoERERETkAAMlIiIiIgf+HwNvWb+23dT1AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", @@ -842,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -871,9 +342,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.17" } }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/docs/apidoc/dolfyn.binners.rst b/docs/apidoc/dolfyn.binners.rst index 34dbb0ec..fccf02a5 100644 --- a/docs/apidoc/dolfyn.binners.rst +++ b/docs/apidoc/dolfyn.binners.rst @@ -21,12 +21,12 @@ Below is a list of functions that can be called from `VelBinner`. ~dolfyn.binned.TimeBinner.std ~dolfyn.velocity.VelBinner.do_avg ~dolfyn.velocity.VelBinner.do_var + ~dolfyn.velocity.VelBinner.calc_ti + ~dolfyn.velocity.VelBinner.calc_psd ~dolfyn.velocity.VelBinner.calc_coh ~dolfyn.velocity.VelBinner.calc_phase_angle ~dolfyn.velocity.VelBinner.calc_acov ~dolfyn.velocity.VelBinner.calc_xcov - ~dolfyn.velocity.VelBinner.calc_tke - ~dolfyn.velocity.VelBinner.calc_psd ~dolfyn.binned.TimeBinner.calc_freq ~dolfyn.binned.TimeBinner.calc_psd_base ~dolfyn.binned.TimeBinner.calc_csd_base @@ -43,6 +43,7 @@ Functions for analyzing ADV data via the `ADVBinner` class, beyond those describ ~dolfyn.adv.turbulence.ADVBinner ~dolfyn.adv.turbulence.calc_turbulence ~dolfyn.adv.turbulence.ADVBinner.calc_csd + ~dolfyn.velocity.VelBinner.calc_tke ~dolfyn.adv.turbulence.ADVBinner.calc_stress ~dolfyn.adv.turbulence.ADVBinner.calc_doppler_noise ~dolfyn.adv.turbulence.ADVBinner.check_turbulence_cascade_slope @@ -64,7 +65,6 @@ Functions for analyzing ADCP data via the `ADPBinner` class, beyond those descri ~dolfyn.adp.turbulence.ADPBinner.calc_doppler_noise ~dolfyn.adp.turbulence.ADPBinner.calc_stress_4beam ~dolfyn.adp.turbulence.ADPBinner.calc_stress_5beam - ~dolfyn.adp.turbulence.ADPBinner.calc_total_tke ~dolfyn.adp.turbulence.ADPBinner.check_turbulence_cascade_slope ~dolfyn.adp.turbulence.ADPBinner.calc_dissipation_LT83 ~dolfyn.adp.turbulence.ADPBinner.calc_dissipation_SF diff --git a/dolfyn/adp/turbulence.py b/dolfyn/adp/turbulence.py index 4f2ef702..5ff46e0f 100644 --- a/dolfyn/adp/turbulence.py +++ b/dolfyn/adp/turbulence.py @@ -238,9 +238,10 @@ def calc_doppler_noise(self, psd, pct_fN=0.8): N2 = psd.sel(freq=f_range) * psd.freq.sel(freq=f_range) noise_level = np.sqrt(N2.mean(dim='freq')) + time_coord = psd.dims[0] # no reason this shouldn't be time or time_b5 return xr.DataArray( noise_level.values.astype('float32'), - dims=['time'], + coords={time_coord: psd.coords[time_coord]}, attrs={'units': 'm s-1', 'long_name': 'Doppler Noise Level', 'description': 'Doppler noise level calculated ' @@ -439,7 +440,7 @@ def calc_stress_5beam(self, ds, noise=None, orientation=None, beam_angle=None, t in pitch and roll. u'v'_ cannot be directly calculated by a 5-beam ADCP, so it is approximated by the covariance of `u` and `v`. The uncertainty introduced by using this approximation is small if deviations from pitch - and roll are small (< 10 degrees). + and roll are small (<= 5 degrees). Dewey, R., and S. Stringer. "Reynolds stresses and turbulent kinetic energy estimates from various ADCP beam configurations: Theory." J. of @@ -455,7 +456,7 @@ def calc_stress_5beam(self, ds, noise=None, orientation=None, beam_angle=None, t # Run through warnings b_angle, noise = self._stress_func_warnings( - ds, beam_angle, noise, tilt_thresh=10) + ds, beam_angle, noise, tilt_thresh=5) # Fetch beam order beam_order, phi2, phi3 = self._check_orientation( @@ -522,47 +523,6 @@ def calc_stress_5beam(self, ds, noise=None, orientation=None, beam_angle=None, t return tke_vec, stress_vec - def calc_total_tke(self, ds, noise=None, orientation=None, beam_angle=None): - """Calculate magnitude of turbulent kinetic energy from 5-beam ADCP. - - Parameters - ---------- - ds : xarray.Dataset - Raw dataset in beam coordinates - ds_avg : xarray.Dataset - Binned dataset in final coordinate reference frame - noise : int or xarray.DataArray, default=0 (time) - Doppler noise level in units of m/s - orientation : str, default=ds.attrs['orientation'] - Direction ADCP is facing ('up' or 'down') - beam_angle : int, default=ds.attrs['beam_angle'] - ADCP beam angle in units of degrees - - Returns - ------- - tke : xarray.DataArray - Turbulent kinetic energy magnitude - - Notes - ----- - This function is a wrapper around 'calc_stress_5beam' that then - combines the TKE components. - - Warning: the integral length scale of turbulence captured by the - ADCP measurements (i.e. the size of turbulent structures) increases - with increasing range from the instrument. - """ - - tke_vec = self.calc_stress_5beam( - ds, noise, orientation, beam_angle, tke_only=True) - - tke = tke_vec.sum('tke') / 2 - tke.attrs['units'] = 'm2 s-2' - tke.attrs['long_name'] = 'TKE Magnitude', - tke.attrs['standard_name'] = 'specific_turbulent_kinetic_energy_of_sea_water' - - return tke.astype('float32') - def check_turbulence_cascade_slope(self, psd, freq_range=[0.2, 0.4]): """This function calculates the slope of the PSD, the power spectra of velocity, within the given frequency range. The purpose of this @@ -623,7 +583,7 @@ def check_turbulence_cascade_slope(self, psd, freq_range=[0.2, 0.4]): return m, b - def calc_dissipation_LT83(self, psd, U_mag, freq_range=[0.2, 0.4]): + def calc_dissipation_LT83(self, psd, U_mag, freq_range=[0.2, 0.4], noise=None): """Calculate the TKE dissipation rate from the velocity spectra. Parameters @@ -633,8 +593,12 @@ def calc_dissipation_LT83(self, psd, U_mag, freq_range=[0.2, 0.4]): U_mag : xarray.DataArray (time) The bin-averaged horizontal velocity (a.k.a. speed) from a single depth bin (range) f_range : iterable(2) - The range over which to integrate/average the spectrum, in units + The range over which to integrate/average the spectrum, in units of the psd frequency vector (Hz or rad/s) + noise : float or array-like + Instrument noise level in same units as velocity. Typically + found from `adp.turbulence.calc_doppler_noise`. + Default: None. Returns ------- @@ -669,6 +633,20 @@ def calc_dissipation_LT83(self, psd, U_mag, freq_range=[0.2, 0.4]): raise Exception('PSD should be 2-dimensional (time, frequency)') if len(U_mag.shape) != 1: raise Exception('U_mag should be 1-dimensional (time)') + if not hasattr(freq_range, "__iter__") or len(freq_range) != 2: + raise ValueError("`freq_range` must be an iterable of length 2.") + if noise is not None: + if np.shape(noise)[0] != np.shape(psd)[0]: + raise Exception( + 'Noise should have same first dimension as PSD') + else: + noise = np.array(0) + + # Noise subtraction from binner.TimeBinner.calc_psd_base + psd = psd.copy() + if noise is not None: + psd -= noise**2 / (self.fs / 2) + psd = psd.where(psd > 0, np.min(np.abs(psd)) / 100) freq = psd.freq idx = np.where((freq_range[0] < freq) & (freq < freq_range[1])) @@ -853,7 +831,7 @@ def calc_ustar_fit(self, ds_avg, upwp_, z_inds=slice(1, 5), H=None): """ if not H: - H = ds_avg.depth.values + H = ds_avg["depth"].values z = ds_avg['range'].values upwp_ = upwp_.values @@ -863,6 +841,6 @@ def calc_ustar_fit(self, ds_avg, upwp_, z_inds=slice(1, 5), H=None): return xr.DataArray( u_star.astype('float32'), - coords={'time': ds_avg.time}, + coords={'time': ds_avg["time"]}, attrs={'units': 'm s-1', 'long_name': 'Friction Velocity'}) diff --git a/dolfyn/adv/turbulence.py b/dolfyn/adv/turbulence.py index ada19540..1f4ccaec 100644 --- a/dolfyn/adv/turbulence.py +++ b/dolfyn/adv/turbulence.py @@ -21,8 +21,10 @@ class ADVBinner(VelBinner): The length of the FFT for computing spectra (must be <= n_bin) n_fft_coh : int (optional, default: `n_fft_coh`=`n_fft`) Number of data points to use for coherence and cross-spectra ffts - noise : float, list or numpy.ndarray - Instrument's doppler noise in same units as velocity + noise : float or array-like + Instrument noise level in same units as velocity. Typically + found from `adv.turbulence.calc_doppler_noise`. + Default: None. """ def __call__(self, ds, freq_units='rad/s', window='hann'): @@ -49,7 +51,7 @@ def __call__(self, ds, freq_units='rad/s', window='hann'): def calc_stress(self, veldat, detrend=True): """ - Calculate the stresses (covariances of u,v,w) + Calculate the stresses (covariances of u,v,w in m^2/s^2) Parameters ---------- @@ -165,7 +167,7 @@ def calc_csd(self, veldat, 'time': time, 'coh_freq': coh_freq}, dims=['C', 'time', 'coh_freq'], - attrs={'units': units, + attrs={'units': units, 'n_fft_coh': n_fft, 'long_name': 'Cross Spectral Density'}) csd['coh_freq'].attrs['units'] = freq_units @@ -230,7 +232,7 @@ def calc_doppler_noise(self, psd, pct_fN=0.8): return xr.DataArray( noise_level.values.astype('float32'), - dims=['dir', 'time'], + coords={'S': psd['S'], 'time': psd['time']}, attrs={'units': 'm/s', 'long_name': 'Doppler Noise Level', 'description': 'Doppler noise level calculated ' @@ -296,7 +298,7 @@ def check_turbulence_cascade_slope(self, psd, freq_range=[6.28, 12.57]): return m, b - def calc_epsilon_LT83(self, psd, U_mag, freq_range=[6.28, 12.57]): + def calc_epsilon_LT83(self, psd, U_mag, freq_range=[6.28, 12.57], noise=None): """Calculate the dissipation rate from the PSD Parameters @@ -308,6 +310,10 @@ def calc_epsilon_LT83(self, psd, U_mag, freq_range=[6.28, 12.57]): freq_range : iterable(2) (default: [6.28, 12.57]) The range over which to integrate/average the spectrum, in units of the psd frequency vector (Hz or rad/s) + noise : float or array-like + Instrument noise level in same units as velocity. Typically + found from `adv.turbulence.calc_doppler_noise`. + Default: None. Returns ------- @@ -339,8 +345,23 @@ def calc_epsilon_LT83(self, psd, U_mag, freq_range=[6.28, 12.57]): """ # Ensure time has been averaged - if len(psd.time)!=len(U_mag.time): + if len(psd.time) != len(U_mag.time): raise Exception("`U_mag` should be from ensembled-averaged dataset") + if not hasattr(freq_range, "__iter__") or len(freq_range) != 2: + raise ValueError("`freq_range` must be an iterable of length 2.") + + if noise is not None: + if np.shape(noise)[0] != 3: + raise Exception( + 'Noise should have same first dimension as velocity') + else: + noise = np.array([0, 0, 0])[:, None, None] + + # Noise subtraction from binner.TimeBinner.calc_psd_base + psd = psd.copy() + if noise is not None: + psd -= (noise**2 / (self.fs / 2)) + psd = psd.where(psd > 0, np.min(np.abs(psd)) / 100) freq = psd.freq idx = np.where((freq_range[0] < freq) & (freq < freq_range[1])) diff --git a/dolfyn/binned.py b/dolfyn/binned.py index 4b6cd8c8..d61e9358 100644 --- a/dolfyn/binned.py +++ b/dolfyn/binned.py @@ -401,7 +401,7 @@ def calc_psd_base(self, dat, fs=None, window='hann', noise=0, for slc in slice1d_along_axis(dat.shape, -1): out[slc] = psd(dat[slc], n_fft, fs, window=window, step=step) - if noise != 0: + if np.any(noise): out -= noise**2 / (fs/2) # Make sure all values of the PSD are >0 (but still small): out[out < 0] = np.min(np.abs(out)) / 100 diff --git a/dolfyn/example_data/dual_profile.ad2cp b/dolfyn/example_data/dual_profile.ad2cp new file mode 100644 index 00000000..35567ab4 --- /dev/null +++ b/dolfyn/example_data/dual_profile.ad2cp @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:931634019cc31aea0716dd4ef6b157618611569c4ac8fb6de4b136e2cf1f9005 +size 306869 diff --git a/dolfyn/io/api.py b/dolfyn/io/api.py index cd3a6823..caeca76c 100644 --- a/dolfyn/io/api.py +++ b/dolfyn/io/api.py @@ -192,7 +192,7 @@ def save(ds, filename, if compression: # New netcdf4-c cannot compress variable length strings - if isinstance(ds[ky].data[0], str): + if ds[ky].size <= 1 or isinstance(ds[ky].data[0], str): continue enc[ky].update(dict(zlib=True, complevel=1)) @@ -219,6 +219,12 @@ def load(filename): """ filename = _check_file_ext(filename, 'nc') + + file_type = _get_filetype(filename) + if file_type == '': + raise IOError("File '{}' looks like a git-lfs pointer. You may need to " + "install and initialize git-lfs. See https://git-lfs.github.com" + " for details.".format(filename)) ds = xr.load_dataset(filename, engine='netcdf4') diff --git a/dolfyn/io/base.py b/dolfyn/io/base.py index 1fd8a447..3616f789 100644 --- a/dolfyn/io/base.py +++ b/dolfyn/io/base.py @@ -115,46 +115,51 @@ def _create_dataset(data): readers. Direction 'dir' coordinates are set in `set_coords` """ - ds = xr.Dataset() - tag = ['_avg', '_b5', '_echo', '_bt', '_gps', '_altraw', '_sl'] + tag = ['_avg', '_b5', '_echo', '_bt', '_gps', '_altraw', '_altraw_avg', '_sl'] - FoR = {} - try: - beams = data['attrs']['n_beams'] - except: + ds_dict = {} + for key in data['coords']: + ds_dict[key] = {"dims": (key), "data": data['coords'][key]} + + # Set various coordinate frames + if 'n_beams_avg' in data['attrs']: beams = data['attrs']['n_beams_avg'] + else: + beams = data['attrs']['n_beams'] n_beams = max(min(beams, 4), 3) - beams = np.arange(1, n_beams+1, dtype=np.int32) - FoR['beam'] = xr.DataArray(beams, dims=['beam'], name='beam', attrs={ - 'units': '1', 'long_name': 'Beam Reference Frame'}) - FoR['dir'] = xr.DataArray(beams, dims=['dir'], name='dir', attrs={ - 'units': '1', 'long_name': 'Reference Frame'}) + beams = np.arange(1, n_beams + 1, dtype=np.int32) + + ds_dict['beam'] = {"dims": ('beam'), "data": beams} + ds_dict['dir'] = {"dims": ('dir'), "data": beams} + data['units'].update({'beam': '1', 'dir': '1'}) + data['long_name'].update({'beam': 'Beam Reference Frame', + 'dir': 'Reference Frame'}) + # Iterate through data variables and add them to new dictionary for key in data['data_vars']: # orientation matrices if 'mat' in key: if 'inst' in key: # beam2inst & inst2head orientation matrices - ds[key] = xr.DataArray(data['data_vars'][key], - coords={'x1': beams, 'x2': beams}, - dims=['x1', 'x2'], - attrs={'units': '1', - 'long_name': 'Rotation Matrix'}) + if 'x1' not in ds_dict: + ds_dict['x1'] = {"dims": ('x1'), "data": beams} + ds_dict['x2'] = {"dims": ('x2'), "data": beams} + + ds_dict[key] = {"dims": ('x1', 'x2'), "data": data['data_vars'][key]} + data['units'].update({key: '1'}) + data['long_name'].update({key: 'Rotation Matrix'}) + elif 'orientmat' in key: # earth2inst orientation matrix if any(val in key for val in tag): tg = '_' + key.rsplit('_')[-1] else: tg = '' - earth = xr.DataArray(['E', 'N', 'U'], dims=['earth'], name='earth', attrs={ - 'units': '1', 'long_name': 'Earth Reference Frame'}) - inst = xr.DataArray(['X', 'Y', 'Z'], dims=['inst'], name='inst', attrs={ - 'units': '1', 'long_name': 'Instrument Reference Frame'}) - time = data['coords']['time'+tg] - ds[key] = xr.DataArray(data['data_vars'][key], - coords={'earth': earth, - 'inst': inst, 'time'+tg: time}, - dims=['earth', 'inst', 'time'+tg], - attrs={'units': data['units']['orientmat'], - 'long_name': data['long_name']['orientmat']}) + ds_dict['earth'] = {"dims": ('earth'), "data": ['E', 'N', 'U']} + ds_dict['inst'] = {"dims": ('inst'), "data": ['X', 'Y', 'Z']} + ds_dict[key] = {"dims": ('earth', 'inst', 'time' + tg), "data": data['data_vars'][key]} + data["units"].update({"earth": "1", "inst": "1", key: data["units"]["orientmat"]}) + data["long_name"].update({"earth": "Earth Reference Frame", + "inst": "Instrument Reference Frame", + key: data["long_name"]["orientmat"]}) # quaternion units never change elif 'quaternions' in key: @@ -162,52 +167,35 @@ def _create_dataset(data): tg = '_' + key.rsplit('_')[-1] else: tg = '' - q = xr.DataArray(['w', 'x', 'y', 'z'], dims=['q'], name='q', attrs={ - 'units': '1', 'long_name': 'Quaternion Vector Components'}) - time = data['coords']['time'+tg] - ds[key] = xr.DataArray(data['data_vars'][key], - coords={'q': q, - 'time'+tg: time}, - dims=['q', 'time'+tg], - attrs={'units': data['units']['quaternions'], - 'long_name': data['long_name']['quaternions']}) - else: - # Assign each variable to a dataArray - ds[key] = xr.DataArray(data['data_vars'][key]) - # Assign metadata to each dataArray - for md in ['units', 'long_name', 'standard_name']: - if key in data[md]: - ds[key].attrs[md] = data[md][key] - try: # make sure ones with tags get units - tg = '_' + key.rsplit('_')[-1] - if any(val in key for val in tag): - ds[key].attrs[md] = data[md][key[:-len(tg)]] - except: - pass - # Fill in dimensions and coordinates for each dataArray + if 'q' not in ds_dict: + ds_dict['q'] = {"dims": ("q"), "data": ['w', 'x', 'y', 'z']} + data['units'].update({'q': '1'}) + data['long_name'].update({'q': 'Quaternion Vector Components'}) + + ds_dict[key] = {"dims": ("q", "time" + tg), "data": data['data_vars'][key]} + data['units'].update({key: data['units']['quaternions']}) + data['long_name'].update({key: data['long_name']['quaternions']}) + + else: shp = data['data_vars'][key].shape - l = len(shp) - if l == 1: # 1D variables - if any(val in key for val in tag): + if len(shp) == 1: # 1D variables + if '_altraw_avg' in key: + tg = '_altraw_avg' + elif any(val in key for val in tag): tg = '_' + key.rsplit('_')[-1] else: tg = '' - ds[key] = ds[key].rename({'dim_0': 'time'+tg}) - ds[key] = ds[key].assign_coords( - {'time'+tg: data['coords']['time'+tg]}) + ds_dict[key] = {"dims": ("time" + tg), "data": data['data_vars'][key]} - elif l == 2: # 2D variables + elif len(shp) == 2: # 2D variables if key == 'echo': - ds[key] = ds[key].rename({'dim_0': 'range_echo', - 'dim_1': 'time_echo'}) - ds[key] = ds[key].assign_coords({'range_echo': data['coords']['range_echo'], - 'time_echo': data['coords']['time_echo']}) - elif key=='samp_altraw': # raw altimeter samples - ds[key] = ds[key].rename({'dim_0': 'n_altraw', - 'dim_1': 'time_altraw'}) - ds[key] = ds[key].assign_coords({'time_altraw': data['coords']['time_altraw']}) - + ds_dict[key] = {"dims": ("range_echo", "time_echo"), "data": data['data_vars'][key]} + elif key == 'samp_altraw': + ds_dict[key] = {"dims": ("n_altraw", "time_altraw"), "data": data['data_vars'][key]} + elif key == 'samp_altraw_avg': + ds_dict[key] = {"dims": ("n_altraw_avg", "time_altraw_avg"), "data": data['data_vars'][key]} + # ADV/ADCP instrument vector data, bottom tracking elif shp[0] == n_beams and not any(val in key for val in tag[:3]): if 'bt' in key and 'time_bt' in data['coords']: @@ -218,10 +206,8 @@ def _create_dataset(data): dim0 = 'beam' else: dim0 = 'dir' - ds[key] = ds[key].rename({'dim_0': dim0, - 'dim_1': 'time'+tg}) - ds[key] = ds[key].assign_coords({dim0: FoR[dim0], - 'time'+tg: data['coords']['time'+tg]}) + ds_dict[key] = {"dims": (dim0, "time" + tg), "data": data['data_vars'][key]} + # ADCP IMU data elif shp[0] == 3: if not any(val in key for val in tag): @@ -229,16 +215,18 @@ def _create_dataset(data): else: tg = [val for val in tag if val in key] tg = tg[0] - dirIMU = xr.DataArray([1, 2, 3], dims=['dirIMU'], name='dirIMU', attrs={ - 'units': '1', 'long_name': 'Reference Frame'}) - ds[key] = ds[key].rename({'dim_0': 'dirIMU', - 'dim_1': 'time'+tg}) - ds[key] = ds[key].assign_coords({'dirIMU': dirIMU, - 'time'+tg: data['coords']['time'+tg]}) - ds[key].attrs['coverage_content_type'] = 'physicalMeasurement' + if 'dirIMU' not in ds_dict: + ds_dict['dirIMU'] = {"dims": ("dirIMU"), "data": [1, 2, 3]} + data['units'].update({'dirIMU': '1'}) + data['long_name'].update({'dirIMU': 'Reference Frame'}) + + ds_dict[key] = {"dims": ("dirIMU", "time" + tg), "data": data['data_vars'][key]} - elif l == 3: # 3D variables + elif 'b5' in tg: + ds_dict[key] = {"dims": ("range_b5", "time_b5"), "data": data['data_vars'][key]} + + elif len(shp) == 3: # 3D variables if 'vel' in key: dim0 = 'dir' else: # amp, corr, prcnt_gd, status @@ -249,32 +237,34 @@ def _create_dataset(data): tg = '_avg' else: tg = '' - ds[key] = ds[key].rename({'dim_0': dim0, - 'dim_1': 'range'+tg, - 'dim_2': 'time'+tg}) - ds[key] = ds[key].assign_coords({dim0: FoR[dim0], - 'range'+tg: data['coords']['range'+tg], - 'time'+tg: data['coords']['time'+tg]}) + ds_dict[key] = {"dims": (dim0, "range" + tg, "time" + tg), "data": data['data_vars'][key]} + elif 'b5' in key: - # xarray can't handle coords of length 1 - ds[key] = ds[key][0] - ds[key] = ds[key].rename({'dim_1': 'range_b5', - 'dim_2': 'time_b5'}) - ds[key] = ds[key].assign_coords({'range_b5': data['coords']['range_b5'], - 'time_b5': data['coords']['time_b5']}) + # "vel_b5" sometimes stored as (1, range_b5, time_b5) + ds_dict[key] = {"dims": ("range_b5", "time_b5"), "data": data['data_vars'][key][0]} elif 'sl' in key: - ds[key] = ds[key].rename({'dim_0': dim0, - 'dim_1': 'range_sl', - 'dim_2': 'time'}) - ds[key] = ds[key].assign_coords({'range_sl': data['coords']['range_sl'], - 'time': data['coords']['time']}) + ds_dict[key] = {"dims": (dim0, "range_sl", "time"), "data": data['data_vars'][key]} else: - ds = ds.drop_vars(key) warnings.warn(f'Variable not included in dataset: {key}') + # Create dataset + ds = xr.Dataset.from_dict(ds_dict) + + # Assign data array attributes + for key in ds.variables: + for md in ['units', 'long_name', 'standard_name']: + if key in data[md]: + ds[key].attrs[md] = data[md][key] + if len(ds[key].shape) > 1: ds[key].attrs['coverage_content_type'] = 'physicalMeasurement' + try: # make sure ones with tags get units + tg = '_' + key.rsplit('_')[-1] + if any(val in key for val in tag): + ds[key].attrs[md] = data[md][key[:-len(tg)]] + except: + pass - # coordinate attributes + # Assign coordinate attributes for ky in ds.dims: ds[ky].attrs['coverage_content_type'] = 'coordinate' r_list = [r for r in ds.coords if 'range' in r] @@ -288,7 +278,7 @@ def _create_dataset(data): ds[ky].attrs['long_name'] = 'Time' ds[ky].attrs['standard_name'] = 'time' - # dataset metadata + # Set dataset metadata ds.attrs = data['attrs'] return ds diff --git a/dolfyn/io/nortek2.py b/dolfyn/io/nortek2.py index 179781f7..2395a232 100644 --- a/dolfyn/io/nortek2.py +++ b/dolfyn/io/nortek2.py @@ -15,7 +15,7 @@ def read_signature(filename, userdata=True, nens=None, rebuild_index=False, - debug=False, **kwargs): + debug=False, dual_profile=False, **kwargs): """Read a Nortek Signature (.ad2cp) datafile Parameters @@ -25,12 +25,14 @@ def read_signature(filename, userdata=True, nens=None, rebuild_index=False, userdata : bool To search for and use a .userdata.json or not nens : None, int or 2-element tuple (start, stop) - Number of pings or ensembles to read from the file. + Number of pings or ensembles to read from the file. Default is None, read entire file rebuild_index : bool (default: False) Force rebuild of dolfyn-written datafile index. Useful for code updates. debug : bool (default: False) Logs debugger ouput if true + dual_profile : bool (default: False) + Set to true if instrument is running multiple profiles Returns ------- @@ -63,9 +65,11 @@ def read_signature(filename, userdata=True, nens=None, rebuild_index=False, userdata = _find_userdata(filename, userdata) - rdr = _Ad2cpReader(filename, rebuild_index=rebuild_index, debug=debug) + rdr = _Ad2cpReader(filename, rebuild_index=rebuild_index, debug=debug, dual_profile=dual_profile) d = rdr.readfile(nens[0], nens[1]) rdr.sci_data(d) + if rdr._dp: + _clean_dp_skips(d) out = _reorg(d) _reduce(out) @@ -112,20 +116,25 @@ def read_signature(filename, userdata=True, nens=None, rebuild_index=False, logging.root.removeHandler(handler) handler.close() - return ds + # Return two datasets if dual profile + if rdr._dp: + return split_dp_datasets(ds) + else: + return ds class _Ad2cpReader(): def __init__(self, fname, endian=None, bufsize=None, rebuild_index=False, - debug=False): + debug=False, dual_profile=False): self.fname = fname self.debug = debug self._check_nortek(endian) self.f.seek(0, 2) # Seek to end self._eof = self.f.tell() - self._index = lib.get_index(fname, - reload=rebuild_index, - debug=debug) + self._index, self._dp = lib.get_index(fname, + rebuild=rebuild_index, + debug=debug, + dp=dual_profile) self._reopen(bufsize) self.filehead_config = self._read_filehead_config_string() self._ens_pos = self._index['pos'][lib._boolarray_firstensemble_ping( @@ -216,17 +225,21 @@ def _init_burst_readers(self, ): def init_data(self, ens_start, ens_stop): outdat = {} nens = int(ens_stop - ens_start) - - # ID 26 usually only recorded in first ensemble - n26 = ((self._index['ID'] == 26) & - (self._index['ens'] >= ens_start) & - (self._index['ens'] < ens_stop)).sum() - if not n26 and 26 in self._burst_readers: + + # ID 26 and 31 recorded infrequently + def n_id(id): + return ((self._index['ID'] == id) & + (self._index['ens'] >= ens_start) & + (self._index['ens'] < ens_stop)).sum() + n_altraw = {26: n_id(26), 31: n_id(31)} + if not n_altraw[26] and 26 in self._burst_readers: self._burst_readers.pop(26) - + if not n_altraw[31] and 31 in self._burst_readers: + self._burst_readers.pop(31) + for ky in self._burst_readers: - if ky == 26: - n = n26 + if (ky == 26) or (ky == 31): + n = n_altraw[ky] ens = np.zeros(n, dtype='uint32') else: ens = np.arange(ens_start, @@ -237,7 +250,7 @@ def init_data(self, ens_start, ens_stop): outdat[ky]['units'] = self._burst_readers[ky].data_units() outdat[ky]['long_name'] = self._burst_readers[ky].data_longnames() outdat[ky]['standard_name'] = self._burst_readers[ky].data_stdnames() - + return outdat def _read_hdr(self, do_cs=False): @@ -269,7 +282,7 @@ def readfile(self, ens_start=0, ens_stop=None): outdat['filehead_config'] = self.filehead_config print('Reading file %s ...' % self.fname) c = 0 - c26 = 0 + c_altraw = {26: 0, 31: 0} self.f.seek(self._ens_pos[ens_start], 0) while True: try: @@ -277,13 +290,13 @@ def readfile(self, ens_start=0, ens_stop=None): except IOError: return outdat id = hdr['id'] - if id in [21, 22, 23, 24, 28]: # "burst data record" (vel + ast), + if id in [21, 22, 23, 24, 28]: # "burst data record" (vel + ast), # "avg data record" (vel_avg + ast_avg), "bottom track data record" (bt), # "interleaved burst data record" (vel_b5), "echosounder record" (echo) self._read_burst(id, outdat[id], c) - elif id in [26]: - # "burst altimeter raw record" (alt_raw) - recorded on nens==0 - rdr = self._burst_readers[26] + elif id in [26, 31]: + # "burst altimeter raw record" (_altraw), "avg altimeter raw record" (_altraw_avg) + rdr = self._burst_readers[id] if not hasattr(rdr, '_nsamp_index'): first_pass = True tmp_idx = rdr._nsamp_index = rdr._names.index('nsamp_alt') @@ -308,21 +321,21 @@ def readfile(self, ens_start=0, ens_stop=None): rdr._cs_struct = defs.Struct( '<' + '{}H'.format(int(rdr.nbyte // 2))) # Initialize the array - outdat[26]['samp_alt'] = defs._nans( + outdat[id]['samp_alt'] = defs._nans( [rdr._N[tmp_idx], - len(outdat[26]['samp_alt'])], + len(outdat[id]['samp_alt'])], dtype=np.uint16) else: if sz != rdr._N[tmp_idx]: raise Exception( "The number of samples in this 'Altimeter Raw' " "burst is different from prior bursts.") - self._read_burst(id, outdat[id], c26) - outdat[id]['ensemble'][c26] = c - c26 += 1 + self._read_burst(id, outdat[id], c_altraw[id]) + outdat[id]['ensemble'][c_altraw[id]] = c + c_altraw[id] += 1 - elif id in [27, 29, 30, 31, 35, 36]: # unknown how to handle - # "bottom track record", DVL, "altimeter record", "avg altimeter raw record", + elif id in [27, 29, 30, 35, 36]: # unknown how to handle + # "bottom track record", DVL, "altimeter record", # "raw echosounder data record", "raw echosounder transmit data record" if self.debug: logging.debug( @@ -387,11 +400,33 @@ def sci_data(self, dat): dnow['vel'] = (dnow['vel'] * 10.0 ** dnow['vel_scale']).astype('float32') - def __exit__(self, type, value, trace,): - self.f.close() - def __enter__(self,): - return self +def _altraw_reorg(outdat, tag=''): + """Submethod for `_reorg` particular to raw altimeter pings (ID 26 and 31) + """ + for ky in list(outdat['data_vars']): + if ky.endswith('raw' + tag) and not ky.endswith('_altraw' + tag): + outdat['data_vars'].pop(ky) + outdat['coords']['time_altraw' + tag] = outdat['coords'].pop('timeraw' + tag) + # convert "signed fractional" to float + outdat['data_vars']['samp_altraw' + tag] = outdat['data_vars']['samp_altraw' + tag].astype('float32') / 2**8 + + # Read altimeter status + outdat['data_vars'].pop('status_altraw' + tag) + status_alt = lib._alt_status2data(outdat['data_vars']['status_alt' + tag]) + for ky in status_alt: + outdat['attrs'][ky + tag] = lib._collapse( + status_alt[ky].astype('uint8'), name=ky) + outdat['data_vars'].pop('status_alt' + tag) + + # Power level index + power = {0: 'high', 1: 'med-high', 2: 'med-low', 3: 'low'} + outdat['attrs']['power_level_alt' + tag] = power[outdat['attrs'].pop('power_level_idx_alt' + tag)] + + # Other attrs + for ky in list(outdat['attrs']): + if ky.endswith('raw' + tag): + outdat['attrs'][ky.split('raw')[0] + '_alt' + tag] = outdat['attrs'].pop(ky) def _reorg(dat): @@ -408,8 +443,9 @@ def _reorg(dat): cfg['inst_make'] = 'Nortek' cfg['inst_type'] = 'ADCP' - for id, tag in [(21, ''), (22, '_avg'), (23, '_bt'), - (24, '_b5'), (26, 'raw'), (28, '_echo')]: + for id, tag in [(21, ''), (22, '_avg'), (23, '_bt'), + (24, '_b5'), (26, 'raw'), (28, '_echo'), + (31, 'raw_avg')]: if id in [24, 26]: collapse_exclude = [0] else: @@ -436,7 +472,7 @@ def _reorg(dat): dnow['usec100'].astype('uint32') * 100) tmp = lib._beams_cy_int2dict( lib._collapse(dnow['beam_config'], exclude=collapse_exclude, - name='beam_config'), 21) + name='beam_config'), 21) # always 21 here cfg['n_cells' + tag] = tmp['n_cells'] cfg['coord_sys_axes' + tag] = tmp['cy'] cfg['n_beams' + tag] = tmp['n_beams'] @@ -473,24 +509,9 @@ def _reorg(dat): # Move 'altimeter raw' data to its own down-sampled structure if 26 in dat: - for ky in list(outdat['data_vars']): - if ky.endswith('raw') and not ky.endswith('_altraw'): - outdat['data_vars'].pop(ky) - outdat['coords']['time_altraw'] = outdat['coords'].pop('timeraw') - outdat['data_vars']['samp_altraw'] = outdat['data_vars']['samp_altraw'].astype('float32') / 2**8 # convert "signed fractional" to float - - # Read altimeter status - outdat['data_vars'].pop('status_altraw') - status_alt = lib._alt_status2data(outdat['data_vars']['status_alt']) - for ky in status_alt: - outdat['attrs'][ky] = lib._collapse( - status_alt[ky].astype('uint8'), name=ky) - outdat['data_vars'].pop('status_alt') - - # Power level index - power = {0: 'high', 1: 'med-high', 2: 'med-low', 3: 'low'} - outdat['attrs']['power_level_alt'] = power[outdat['attrs'].pop( - 'power_level_idx_alt')] + _altraw_reorg(outdat) + if 31 in dat: + _altraw_reorg(outdat, tag='_avg') # Read status data status0_vars = [x for x in outdat['data_vars'] if 'status0' in x] @@ -528,7 +549,7 @@ def _reorg(dat): outdat['attrs'][ky] = lib._collapse( status0_data[ky].astype('uint8'), name=ky) - # Remove status0 variables - keep status variables as they useful for finding missing pings + # Remove status0 variables - keep status variables as they are useful for finding missing pings [outdat['data_vars'].pop(var) for var in status0_vars] # Set coordinate system @@ -555,13 +576,27 @@ def _reorg(dat): return outdat +def _clean_dp_skips(data): + """Removes zeros from interwoven measurements taken in a dual profile + configuration. + """ + for id in data: + if id == 'filehead_config': + continue + # Check where 'ver' is zero (should be 1 (for bt) or 3 (everything else)) + skips = np.where(data[id]['ver'] != 0) + for var in data[id]: + if var not in ['units', 'long_name', 'standard_name']: + data[id][var] = np.squeeze(data[id][var][..., skips], axis=-2) + + def _reduce(data): """This function takes the output from `reorg`, and further simplifies the data. Mostly this is combining system, environmental, and orientation data --- from different data structures within the same ensemble --- by averaging. """ - + dv = data['data_vars'] dc = data['coords'] da = data['attrs'] @@ -577,21 +612,23 @@ def _reduce(data): if 'vel' in dv: dc['range'] = ((np.arange(dv['vel'].shape[1])+1) * - da['cell_size'] + - da['blank_dist']) + da['cell_size'] + + da['blank_dist']) da['fs'] = da['filehead_config']['BURST']['SR'] tmat = da['filehead_config']['XFBURST'] if 'vel_avg' in dv: dc['range_avg'] = ((np.arange(dv['vel_avg'].shape[1])+1) * - da['cell_size_avg'] + - da['blank_dist_avg']) - dv['orientmat'] = dv.pop('orientmat_avg') + da['cell_size_avg'] + + da['blank_dist_avg']) + if 'orientmat' not in dv: + dv['orientmat'] = dv.pop('orientmat_avg') tmat = da['filehead_config']['XFAVG'] da['fs'] = da['filehead_config']['PLAN']['MIAVG'] da['avg_interval_sec'] = da['filehead_config']['AVG']['AI'] da['bandwidth'] = da['filehead_config']['AVG']['BW'] if 'vel_b5' in dv: - dc['range_b5'] = ((np.arange(dv['vel_b5'].shape[1])+1) * + # vel_b5 is sometimes shape 2 and sometimes shape 3 + dc['range_b5'] = ((np.arange(dv['vel_b5'].shape[-2])+1) * da['cell_size_b5'] + da['blank_dist_b5']) if 'echo_echo' in dv: @@ -611,7 +648,7 @@ def _reduce(data): theta = da['filehead_config']['BEAMCFGLIST'][0] if 'THETA=' in theta: da['beam_angle'] = int(theta[13:15]) - + tm = np.zeros((tmat['ROWS'], tmat['COLS']), dtype=np.float32) for irow in range(tmat['ROWS']): for icol in range(tmat['COLS']): @@ -624,3 +661,62 @@ def _reduce(data): if 'time' in val: time = val dc['time'] = dc[time] + + +def split_dp_datasets(ds): + """Splits a dataset containing dual profiles into individual profiles + """ + # Figure out which variables belong to which profile based on length of time variables + t_dict = {} + for t in ds.coords: + if 'time' in t: + t_dict[t] = ds[t].size + + other_coords = [] + for key, val in t_dict.items(): + if val != t_dict['time']: + if key.endswith('altraw'): + # altraw goes with burst, altraw_avg goes with avg + continue + other_coords.append(key) + + # Fetch variables, coordinates, and attrs for second profiling configuration + other_vars = [v for v in ds.data_vars if any(x in ds[v].coords for x in other_coords)] + other_tags = [s.split('_')[-1] for s in other_coords] + other_coords += [v for v in ds.coords if any(x in v for x in other_tags)] + other_attrs = [s for s in ds.attrs if any(x in s for x in other_tags)] + critical_attrs = ['inst_model', + 'inst_make', + 'inst_type', + 'fs', + 'orientation', + 'orient_status', + 'has_imu', + 'beam_angle'] + + # Create second dataset + ds2 = type(ds)() + for a in (other_attrs + critical_attrs): + ds2.attrs[a] = ds.attrs[a] + for v in other_vars: + ds2[v] = ds[v] + # Set rotate_vars + rotate_vars2 = [v for v in ds.attrs['rotate_vars'] if v in other_vars] + ds2.attrs['rotate_vars'] = rotate_vars2 + # Set orientation matricies + ds2['beam2inst_orientmat'] = ds['beam2inst_orientmat'] + ds2 = ds2.rename({'orientmat_' + other_tags[0]: 'orientmat'}) + # Set original coordinate system + cy = ds2.attrs['coord_sys_axes_' + other_tags[0]] + ds2.attrs['coord_sys'] = {'XYZ': 'inst', + 'ENU': 'earth', + 'beam': 'beam'}[cy] + ds2 = _set_coords(ds2, ref_frame=ds2.coord_sys) + + # Clean up first dataset + [ds.attrs.pop(ky) for ky in other_attrs] + ds = ds.drop_vars(other_vars + other_coords) + for itm in rotate_vars2: + ds.attrs['rotate_vars'].remove(itm) + + return ds, ds2 diff --git a/dolfyn/io/nortek2_lib.py b/dolfyn/io/nortek2_lib.py index 6f862196..b72bfb7e 100644 --- a/dolfyn/io/nortek2_lib.py +++ b/dolfyn/io/nortek2_lib.py @@ -101,8 +101,8 @@ def _create_index(infile, outfile, N_ens, debug): fout = open(_abspath(outfile), 'wb') fout.write(b'Index Ver:') fout.write(struct.pack(' 0: - # Covers all id keys saved in "burst mode" - ens[idk] = last_ens[idk]+1 + if last_ens[idk] > 0: + if (ens[idk] == 1) or (ens[idk] < last_ens[idk]): + # Covers all id keys saved in "burst mode" + # Covers ID keys not saved in sequential order + ens[idk] = last_ens[idk] + 1 if last_ens[idk] > 0 and last_ens[idk] != ens[idk]: N[idk] += 1 @@ -140,7 +142,8 @@ def _create_index(infile, outfile, N_ens, debug): fin.seek(dat[4] - (36 + seek_2ens[idk]), 1) last_ens[idk] = ens[idk] - if debug and N[idk] < 5: + if debug: + # File Position: Valid ID keys (1A, 10), Hex ID, Length in bytes, Ensemble #, Last Ensemble Found' # hex: [18, 15, 1C, 17] = [vel_b5, vel, echo, bt] logging.info('%10d: %02X, %d, %02X, %d, %d, %d, %d\n' % (pos, dat[0], dat[1], dat[2], dat[4], @@ -152,7 +155,7 @@ def _create_index(infile, outfile, N_ens, debug): print(" Done.") -def _check_index(idx, infile, fix_hw_ens=False): +def _check_index(idx, infile, fix_hw_ens=False, dp=False): uid = np.unique(idx['ID']) if fix_hw_ens: hwe = idx['hw_ens'] @@ -162,13 +165,29 @@ def _check_index(idx, infile, fix_hw_ens=False): ens = idx['ens'] N_id = len(uid) FLAG = False + + # Are there better ways to detect dual profile? + if (21 in uid) and (22 in uid): + warnings.warn("Dual Profile detected... Two datasets will be returned.") + dp = True + # This loop fixes 'skips' inside the file for id in uid: # These are the indices for this ID inds = np.nonzero(idx['ID'] == id)[0] - # These are bad steps in the indices for this ID ibad = np.nonzero(np.diff(inds) > N_id)[0] + # Check if spacing is equal for dual profiling ADCPs + if dp: + skip_size = np.diff(ibad) + n_skip, count = np.unique(skip_size, return_counts=True) + # If multiple skips are of the same size, assume okay + for n, c in zip(n_skip, count): + if c > 1: + skip_size[skip_size == n] = 0 + # assume last "ibad" element is always good for dp's + mask = np.append(skip_size, 0).astype(bool) if any(skip_size) else [] + ibad = ibad[mask] for ib in ibad: FLAG = True # The ping number reported here may not be quite right if @@ -178,16 +197,20 @@ def _check_index(idx, infile, fix_hw_ens=False): hwe[inds[(ib + 1):]] += 1 ens[inds[(ib + 1):]] += 1 - # This block fixes skips that originate from before this file. - delta = max(hwe[:N_id]) - hwe[:N_id] - for d, id in zip(delta, idx['ID'][:N_id]): - if d != 0: - FLAG = True - hwe[id == idx['ID']] += d - ens[id == idx['ID']] += d + # if not dp: + # # This block fixes skips that originate from before this file. + # # Check first N id's and correct + # delta = max(hwe[:N_id]) - hwe[:N_id] + # for d, id in zip(delta, idx['ID'][:N_id]): + # if d != 0: + # FLAG = True + # hwe[id == idx['ID']] += d + # ens[id == idx['ID']] += d + + # if np.any(np.diff(ens) > 1) and FLAG: + # idx['ens'] = np.unwrap(hwe.astype(np.int64), period=period) - hwe[0] - if np.any(np.diff(ens) > 1) and FLAG: - idx['ens'] = np.unwrap(hwe.astype(np.int64), period=period) - hwe[0] + return dp def _boolarray_firstensemble_ping(index): @@ -199,7 +222,7 @@ def _boolarray_firstensemble_ping(index): return dens -def get_index(infile, reload=False, debug=False): +def get_index(infile, rebuild=False, debug=False, dp=False): """This function reads ad2cp.index files Parameters @@ -218,7 +241,7 @@ def get_index(infile, reload=False, debug=False): """ index_file = infile + '.index' - if not path.isfile(index_file) or reload: + if not path.isfile(index_file) or rebuild or debug: _create_index(infile, index_file, 2 ** 32, debug) f = open(_abspath(index_file), 'rb') file_head = f.read(12) @@ -230,8 +253,8 @@ def get_index(infile, reload=False, debug=False): f.seek(0, 0) out = np.fromfile(f, dtype=_index_dtype[index_ver]) f.close() - _check_index(out, infile) - return out + dp = _check_index(out, infile, dp=dp) + return out, dp def crop_ensembles(infile, outfile, range): @@ -252,7 +275,7 @@ def crop_ensembles(infile, outfile, range): 2 element list of start and end ensemble (or time index) """ - idx = get_index(infile) + idx, dp = get_index(infile) with open(_abspath(infile), 'rb') as fin: with open(_abspath(outfile), 'wb') as fout: fout.write(fin.read(idx['pos'][0])) @@ -414,7 +437,8 @@ def _beams_cy_int2dict(val, id): """ if id == 28: # 0x1C (echosounder) return dict(n_cells=val) - + elif id in [26, 31]: + return dict(n_cells=val & (2**10 - 1), cy="beam", n_beams=1) return dict( n_cells=val & (2 ** 10 - 1), cy=['ENU', 'XYZ', 'beam', None][val >> 10 & 3], @@ -451,7 +475,7 @@ def _collapse(vec, name=None, exclude=[]): "Values found: {} (counts: {}).\n" "Using the most common value: {}".format( name, list(uniq), list(counts), val)) - + return val @@ -468,24 +492,33 @@ def _calc_config(index): ids = np.unique(index['ID']) config = {} for id in ids: - if id not in [21, 22, 23, 24, 26, 28]: + if id not in [21, 22, 23, 24, 26, 28, 31]: continue if id == 23: type = 'bt' - elif id == 22: + elif (id == 22) or (id == 31): type = 'avg' else: type = 'burst' inds = index['ID'] == id _config = index['config'][inds] _beams_cy = index['beams_cy'][inds] + # Check that these variables are consistent if not _isuniform(_config): raise Exception("config are not identical for id: 0x{:X}." .format(id)) if not _isuniform(_beams_cy): - raise Exception("beams_cy are not identical for id: 0x{:X}." - .format(id)) + err = True + if id == 23: + # change in "n_cells" doesn't matter + lob = np.unique(_beams_cy) + beams = list(map(_beams_cy_int2dict, lob, 23 * np.ones(lob.size))) + if all([d['cy'] for d in beams]) and all([d['n_beams'] for d in beams]): + err = False + if err: + raise Exception("beams_cy are not identical for id: 0x{:X}.".format(id)) + # Now that we've confirmed they are the same: config[id] = _headconfig_int2dict(_config[0], mode=type) config[id].update(_beams_cy_int2dict(_beams_cy[0], id)) diff --git a/dolfyn/tests/data/BenchFile01.nc b/dolfyn/tests/data/BenchFile01.nc index 12c4ae00..f7f1a410 100644 --- a/dolfyn/tests/data/BenchFile01.nc +++ b/dolfyn/tests/data/BenchFile01.nc @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:80ae1ffdc4659b7eb7088d1a6ac980e4997e053ae1825886177ddaf10c275cdb -size 272340 +oid sha256:aebbea48ca54a8e6d595d1c9f9a6e3c94c0735f0f23e2fd722e00317211486c7 +size 273346 diff --git a/dolfyn/tests/data/BenchFile01_avg.nc b/dolfyn/tests/data/BenchFile01_avg.nc index bca0ffba..e505f17a 100644 --- a/dolfyn/tests/data/BenchFile01_avg.nc +++ b/dolfyn/tests/data/BenchFile01_avg.nc @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:86c32e19441245f8db41b798315394673010dd877429fce2daed8a10a6eb5425 -size 89675 +oid sha256:95288fa8ea36c877df0faab1cd6cc374407c8932495e391584a94d4393aa89e7 +size 89659 diff --git a/dolfyn/tests/data/BenchFile01_rotate_beam2inst.nc b/dolfyn/tests/data/BenchFile01_rotate_beam2inst.nc index c8c7a970..fd02bbeb 100644 --- a/dolfyn/tests/data/BenchFile01_rotate_beam2inst.nc +++ b/dolfyn/tests/data/BenchFile01_rotate_beam2inst.nc @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:addfd9fa3047160fd41d1f06772e3e9ab56a61bcc8ba472ed29122d0de9491cb -size 272360 +oid sha256:081e6a234327c057d84e5659515fe46bc7ee0ff4c6c5a81877c61d355eccfe7c +size 273341 diff --git a/dolfyn/tests/data/BenchFile01_rotate_earth2principal.nc b/dolfyn/tests/data/BenchFile01_rotate_earth2principal.nc index 6fcbd1be..5b0d7c5e 100644 --- a/dolfyn/tests/data/BenchFile01_rotate_earth2principal.nc +++ b/dolfyn/tests/data/BenchFile01_rotate_earth2principal.nc @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:6f0d9e6f4ffa3264bf7d8ee50708603f70e0a740fb27d3aaa682d355e3f90c59 -size 272370 +oid sha256:be3ccf79a637f21f99d840a4254ed439bba43cea7c8e398f18682bdd38591363 +size 273346 diff --git a/dolfyn/tests/data/BenchFile01_rotate_inst2earth.nc b/dolfyn/tests/data/BenchFile01_rotate_inst2earth.nc index a2aa27dd..7f9a277c 100644 --- a/dolfyn/tests/data/BenchFile01_rotate_inst2earth.nc +++ b/dolfyn/tests/data/BenchFile01_rotate_inst2earth.nc @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:8fc6c409b409002393da22c8473898d928569e72c20ee24bd57e634508ff3ca3 -size 272362 +oid sha256:e6ced23cdac6eb11ff9d8496f124e30431a69b2670b0376ced1ab502f594e347 +size 273342 diff --git a/dolfyn/tests/data/Sig1000_IMU_bin.nc b/dolfyn/tests/data/Sig1000_IMU_bin.nc deleted file mode 100644 index af746556..00000000 --- a/dolfyn/tests/data/Sig1000_IMU_bin.nc +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:80d1cc912fe6436e26c2c2cd4999432f4ca7601b7536c4ead8d60a81209b51af -size 88917 diff --git a/dolfyn/tests/data/Sig1000_tidal_bin.nc b/dolfyn/tests/data/Sig1000_tidal_bin.nc new file mode 100644 index 00000000..5e39c75e --- /dev/null +++ b/dolfyn/tests/data/Sig1000_tidal_bin.nc @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0272eb6997d44ae7507a85bd0c66aab6495236eaf35a89646d5e98091ab257e6 +size 111725 diff --git a/dolfyn/tests/data/dual_profile.nc b/dolfyn/tests/data/dual_profile.nc new file mode 100644 index 00000000..2443d898 --- /dev/null +++ b/dolfyn/tests/data/dual_profile.nc @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a1059e8a7b66fe6e0aeb3b7eb700801e3ed76ecb111cb02700479841e3b48e4c +size 148510 diff --git a/dolfyn/tests/data/vector_data01_bin.nc b/dolfyn/tests/data/vector_data01_bin.nc index f61a4ae0..964063cb 100644 --- a/dolfyn/tests/data/vector_data01_bin.nc +++ b/dolfyn/tests/data/vector_data01_bin.nc @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:7ae7534bff86568ba610503afb28c99595351ba004cee3a3811b48d35f112354 -size 46220 +oid sha256:863dc2266cfd403d26114a081cae465bab355f174adcc3a3d109a5ee69e136a3 +size 49894 diff --git a/dolfyn/tests/make_data.py b/dolfyn/tests/make_data.py index f406dead..a8c6c448 100644 --- a/dolfyn/tests/make_data.py +++ b/dolfyn/tests/make_data.py @@ -49,6 +49,7 @@ # ta.test_do_func(make_data=True) # ta.test_calc_func(make_data=True) # ta.test_adv_turbulence(make_data=True) +# ta.test_adcp_turbulence(make_data=True) # ts.test_shortcuts(make_data=True) diff --git a/dolfyn/tests/test_analysis.py b/dolfyn/tests/test_analysis.py index 269fe58c..f9707a39 100644 --- a/dolfyn/tests/test_analysis.py +++ b/dolfyn/tests/test_analysis.py @@ -114,14 +114,16 @@ def test_adv_turbulence(make_data=False): tdat['stress_detrend'] = bnr.calc_stress(dat['vel']) tdat['stress_demean'] = bnr.calc_stress(dat['vel'], detrend=False) - tdat['csd'] = bnr.calc_csd( - dat['vel'], freq_units='rad', window='hamm', n_fft_coh=10) + tdat['csd'] = bnr.calc_csd(dat['vel'], freq_units='rad', window='hamm', n_fft_coh=10) tdat['LT83'] = bnr.calc_epsilon_LT83(tdat['psd'], tdat.velds.U_mag) + tdat['noise'] = bnr.calc_doppler_noise(tdat['psd'], pct_fN=0.8) + tdat['LT83_noise'] = bnr.calc_epsilon_LT83(tdat['psd'], tdat.velds.U_mag, noise=tdat['noise']) tdat['SF'] = bnr.calc_epsilon_SF(dat['vel'][0], tdat.velds.U_mag) tdat['TE01'] = bnr.calc_epsilon_TE01(dat, tdat) tdat['L'] = bnr.calc_L_int(acov, tdat.velds.U_mag) slope_check = bnr.check_turbulence_cascade_slope( tdat['psd'][-1].mean('time'), freq_range=[10, 100]) + tdat['psd_noise'] = bnr.calc_psd(dat['vel'], freq_units='rad', noise=[0.06, 0.04, 0.01]) if make_data: save(tdat, 'vector_data01_bin.nc') @@ -132,36 +134,61 @@ def test_adv_turbulence(make_data=False): def test_adcp_turbulence(make_data=False): - dat = tr.dat_sig_i.copy(deep=True) + dat = tr.dat_sig_tide.copy(deep=True) + dat.velds.rotate2('earth') + dat.attrs['principal_heading'] = apm.calc_principal_heading(dat.vel.mean('range')) bnr = apm.ADPBinner(n_bin=20.0, fs=dat.fs, diff_style='centered') + U_mag = dat.velds.U_mag + dat["U_mag"] = U_mag tdat = bnr.do_avg(dat) - tdat['dudz'] = bnr.calc_dudz(tdat.vel) - tdat['dvdz'] = bnr.calc_dvdz(tdat.vel) - tdat['dwdz'] = bnr.calc_dwdz(tdat.vel) - tdat['tau2'] = bnr.calc_shear2(tdat.vel) + + tdat['dudz'] = bnr.calc_dudz(tdat["vel"]) + tdat['dvdz'] = bnr.calc_dvdz(tdat["vel"]) + tdat['dwdz'] = bnr.calc_dwdz(tdat["vel"]) + tdat['tau2'] = bnr.calc_shear2(tdat["vel"]) + tdat['I'] = tdat.velds.I + tdat['ti'] = bnr.calc_ti(U_mag, detrend=False) + dat.velds.rotate2('beam') + tdat['psd'] = bnr.calc_psd(dat['vel'].isel( - dir=2, range=len(dat.range)//2), freq_units='Hz') + dir=2, range=len(dat["range"])//2), freq_units='Hz') tdat['noise'] = bnr.calc_doppler_noise(tdat['psd'], pct_fN=0.8) tdat['stress_vec4'] = bnr.calc_stress_4beam( dat, noise=tdat['noise'], orientation='up', beam_angle=25) tdat['tke_vec5'], tdat['stress_vec5'] = bnr.calc_stress_5beam( dat, noise=tdat['noise'], orientation='up', beam_angle=25, tke_only=False) - tdat['tke'] = bnr.calc_total_tke( - dat, noise=tdat['noise'], orientation='up', beam_angle=25) + # Back in "inst" coordinate frame now + dat.velds.rotate2("beam") + + tdat['ti_noise'] = bnr.calc_ti(U_mag, detrend=False, noise=tdat['noise']) # This is "negative" for this code check tdat['wpwp'] = bnr.calc_tke(dat['vel_b5'], noise=tdat['noise']) tdat['dissipation_rate_LT83'] = bnr.calc_dissipation_LT83( - tdat['psd'], tdat.velds.U_mag.isel(range=len(dat.range)//2), freq_range=[0.2, 0.4]) + tdat['psd'], tdat["U_mag"].isel(range=len(dat["range"])//2), freq_range=[0.2, 0.4]) + tdat['dissipation_rate_LT83_noise'] = bnr.calc_dissipation_LT83( + tdat['psd'], tdat["U_mag"].isel(range=len(dat["range"])//2), freq_range=[0.2, 0.4], noise=tdat['noise']) tdat['dissipation_rate_SF'], tdat['noise_SF'], tdat['D_SF'] = bnr.calc_dissipation_SF( dat.vel.isel(dir=2), r_range=[1, 5]) - tdat['friction_vel'] = bnr.calc_ustar_fit( - tdat, upwp_=tdat['stress_vec5'].sel(tau='upwp_'), z_inds=slice(1, 5), H=50) + slope_check = bnr.check_turbulence_cascade_slope( tdat['psd'].mean('time'), freq_range=[0.4, 4]) + # Check noise subtraction in psd function + tdat['psd_noise'] = bnr.calc_psd(dat['vel'].isel( + dir=2, range=len(dat["range"])//2), freq_units='Hz', noise=0.01) + + tdat['friction_vel'] = bnr.calc_ustar_fit( + tdat, upwp_=tdat['stress_vec5'].sel(tau='upwp_'), z_inds=slice(1, 5), H=50) if make_data: - save(tdat, 'Sig1000_IMU_bin.nc') + save(tdat, 'Sig1000_tidal_bin.nc') return + with pytest.raises(Exception): + bnr.calc_psd(dat['vel'], freq_units='Hz', noise=0.01) + + with pytest.raises(Exception): + bnr.calc_psd(dat['vel'][0], freq_units='Hz', noise=0.01) + assert np.round(slope_check[0].values, 4), -1.0682 - assert_allclose(tdat, load('Sig1000_IMU_bin.nc'), atol=1e-6) + + assert_allclose(tdat, load('Sig1000_tidal_bin.nc'), atol=1e-6) diff --git a/dolfyn/tests/test_read_adp.py b/dolfyn/tests/test_read_adp.py index 282001af..fb162729 100644 --- a/dolfyn/tests/test_read_adp.py +++ b/dolfyn/tests/test_read_adp.py @@ -33,6 +33,7 @@ dat_sig_skip = load('Sig_SkippedPings01.nc') dat_sig_badt = load('Sig1000_BadTime01.nc') dat_sig5_leiw = load('Sig500_last_ensemble_is_whole.nc') +dat_sig_dp2 = load("dual_profile.nc") def test_io_rdi(make_data=False): @@ -97,6 +98,8 @@ def test_io_nortek2(make_data=False): td_sig_ieb = read('VelEchoBT01.ad2cp', nens=nens) td_sig_ie = read('Sig500_Echo.ad2cp', nens=nens) td_sig_tide = read('Sig1000_tidal.ad2cp', nens=nens) + # Only need to test 2nd dataset + td_sig_dp1, td_sig_dp2 = read("dual_profile.ad2cp") with pytest.warns(UserWarning): # This issues a warning... @@ -119,6 +122,7 @@ def test_io_nortek2(make_data=False): os.remove(tb.exdt('Sig_SkippedPings01.ad2cp.index')) os.remove(tb.exdt('Sig500_last_ensemble_is_whole.ad2cp.index')) os.remove(tb.rfnm('Sig1000_BadTime01.ad2cp.index')) + os.remove(tb.exdt("dual_profile.ad2cp.index")) if make_data: save(td_sig, 'BenchFile01.nc') @@ -130,6 +134,7 @@ def test_io_nortek2(make_data=False): save(td_sig_skip, 'Sig_SkippedPings01.nc') save(td_sig_badt, 'Sig1000_BadTime01.nc') save(td_sig5_leiw, 'Sig500_last_ensemble_is_whole.nc') + save(td_sig_dp2, "dual_profile.nc") return assert_allclose(td_sig, dat_sig, atol=1e-6) @@ -141,6 +146,7 @@ def test_io_nortek2(make_data=False): assert_allclose(td_sig5_leiw, dat_sig5_leiw, atol=1e-6) assert_allclose(td_sig_skip, dat_sig_skip, atol=1e-6) assert_allclose(td_sig_badt, dat_sig_badt, atol=1e-6) + assert_allclose(td_sig_dp2, dat_sig_dp2, atol=1e-6) def test_nortek2_crop(make_data=False): diff --git a/dolfyn/tools/psd.py b/dolfyn/tools/psd.py index 56a7feff..5ac4d4f0 100644 --- a/dolfyn/tools/psd.py +++ b/dolfyn/tools/psd.py @@ -30,12 +30,23 @@ def psd_freq(nfft, fs, full=False): def _getwindow(window, nfft): - if window == 'hann': - window = np.hanning(nfft) - elif window == 'hamm': - window = np.hamming(nfft) - elif window is None or window == 1: + if window is None: window = np.ones(nfft) + elif isinstance(window, (int, float)) and window == 1: + window = np.ones(nfft) + elif isinstance(window, str): + if "hann" in window: + window = np.hanning(nfft) + elif "hamm" in window: + window = np.hamming(nfft) + else: + raise ValueError("Unsupported window type: {}".format(window)) + elif isinstance(window, np.ndarray): + if len(window) != nfft: + raise ValueError("Custom window length must be equal to nfft") + else: + raise ValueError("Invalid window parameter") + return window diff --git a/dolfyn/velocity.py b/dolfyn/velocity.py index 97faebc1..c647c0d8 100644 --- a/dolfyn/velocity.py +++ b/dolfyn/velocity.py @@ -920,6 +920,54 @@ def calc_xcov(self, veldat1, veldat2, npt=1, return da + def calc_ti(self, U_mag, noise=0, thresh=0, detrend=False): + """Calculate noise-corrected turbulence intensity. + + Parameters + ---------- + U_mag : xarray.DataArray + Raw velocity magnitude + noise : numeric + Noise level in m/s + thresh : numeric + Theshold below which TI will not be calculated + detrend : bool (default: False) + Detrend the velocity data (True), or simply de-mean it + (False), prior to computing TI. + """ + + if 'xarray' in type(U_mag).__module__: + U = U_mag.values + if "xarray" in type(noise).__module__: + noise = noise.values + + if detrend: + up = self.detrend(U) + else: + up = self.demean(U) + + # Take RMS and subtract noise + u_rms = np.sqrt(np.nanmean(up**2, axis=-1) - noise**2) + u_mag = self.mean(U) + + ti = np.ma.masked_where(u_mag < thresh, u_rms / u_mag) + + dims = U_mag.dims + coords = {} + for nm in U_mag.dims: + if 'time' in nm: + coords[nm] = self.mean(U_mag[nm].values) + else: + coords[nm] = U_mag[nm].values + + return xr.DataArray( + ti.data.astype('float32'), + coords=coords, + dims=dims, + attrs={'units': '% [0,1]', + 'long_name': 'Turbulence Intensity', + 'comment': f'TI was corrected from a noise level of {noise} m/s'}) + def calc_tke(self, veldat, noise=None, detrend=True): """Calculate the turbulent kinetic energy (TKE) (variances of u,v,w). @@ -934,7 +982,7 @@ def calc_tke(self, veldat, noise=None, detrend=True): the same first dimension as the velocity vector. detrend : bool (default: False) Detrend the velocity data (True), or simply de-mean it - (False), prior to computing tke. Note: the psd routines + (False), prior to computing TKE. Note: the PSD routines use detrend, so if you want to have the same amount of variance here as there use ``detrend=True``. @@ -1010,16 +1058,16 @@ def calc_psd(self, veldat, veldat : xr.DataArray The raw velocity data (of dims 'dir' and 'time'). freq_units : string - Frequency units of the returned spectra in either Hz or rad/s + Frequency units of the returned spectra in either Hz or rad/s (`f` or :math:`\\omega`) fs : float (optional) The sample rate (default: from the binner). window : string or array Specify the window function. Options: 1, None, 'hann', 'hamm' - noise : float or array-like - A vector of the noise levels of the velocity data with - the same first dimension as the velocity vector. + noise : numeric + Instrument noise level in same units as velocity. + Default: 0 (ADCP) or [0, 0, 0] (ADV). n_bin : int (optional) The bin-size (default: from the binner). n_fft : int (optional) @@ -1043,6 +1091,8 @@ def calc_psd(self, veldat, vel = veldat.values if 'xarray' in type(noise).__module__: noise = noise.values + if ("rad" not in freq_units) and ("Hz" not in freq_units): + raise ValueError("`freq_units` should be one of 'Hz' or 'rad/s'") # Create frequency vector, also checks whether using f or omega if 'rad' in freq_units: @@ -1062,14 +1112,16 @@ def calc_psd(self, veldat, ).astype('float32') # Spectra, if input is full velocity or a single array - if len(vel.shape) == 2: - assert vel.shape[0] == 3, "Function can only handle 1D or 3D arrays." \ - " If ADCP data, please select a specific depth bin." - if (noise is not None) and (np.shape(noise)[0] != 3): - raise Exception( - 'Noise should have same first dimension as velocity') + if len(vel.shape) >= 2: + if vel.shape[0] != 3: + raise ValueError("Function can only handle 1D or 3D arrays." + " If ADCP data, please select a specific depth bin.") + if noise is not None: + if np.size(noise) != 3: + raise ValueError('Noise is expected to be an array of 3 scalars') else: noise = np.array([0, 0, 0]) + out = np.empty(self._outshape_fft(vel[:3].shape, n_fft=n_fft, n_bin=n_bin), dtype=np.float32) for idx in range(3): @@ -1086,11 +1138,11 @@ def calc_psd(self, veldat, 'freq': freq} dims = ['S', 'time', 'freq'] else: - if (noise is not None) and (len(np.shape(noise)) > 1): - raise Exception( - 'Noise should have same first dimension as velocity') + if noise is not None: + if np.size(noise) > 1: + raise ValueError('Noise is expected to be a scalar') else: - noise = np.array(0) + noise = 0 out = self.calc_psd_base(vel, fs=fs, noise=noise, diff --git a/environment.yml b/environment.yml index 9a38f2d2..ea4482c8 100644 --- a/environment.yml +++ b/environment.yml @@ -3,11 +3,7 @@ channels: - defaults dependencies: - pip - - python>=3.8 + - python < 3.12 - pytest - pip: - - numpy>=1.21 - - scipy>=1.7.0 - - xarray>=0.19.0 - - netCDF4 - - bottleneck + - "-r requirements.txt" diff --git a/requirements.txt b/requirements.txt index b1fb2995..60d32812 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy>=1.21 scipy>=1.7.0 xarray>=0.19.0 -netCDF4 +netCDF4>=1.5.8 bottleneck