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EcoFOCIpy ADCP QC - 20BSP-14A

• mostly remove >100 cm/s flows in u/v

import xarray as xa
import numpy as np

import matplotlib.pyplot as plt

file_in = '20bsp14a_0031m.nc'
xdf = xa.load_dataset(file_in)

xdf =xdf.where(xdf.u_curr_comp <=100,np.nan)
xdf

<xarray.Dataset>
Dimensions:          (latitude: 1, longitude: 1, time: 34591, depth: 20)
Coordinates:
  * time             (time) datetime64[ns] 2020-09-18T08:00:00 ... 2022-09-08...
  * depth            (depth) float64 -10.0 -8.0 -6.0 -4.0 ... 24.0 26.0 28.0
  * latitude         (latitude) float64 64.0
  * longitude        (longitude) float64 -167.9
Data variables:
    u_curr_comp      (latitude, longitude, time, depth) float64 -6.515 ... 0....
    v_curr_comp      (latitude, longitude, time, depth) float64 -13.85 ... 15.09
    w_curr_comp      (latitude, longitude, time, depth) float64 8.9 7.1 ... -0.1
    w_curr_comp_err  (latitude, longitude, time, depth) float64 -3.6 0.0 ... 0.4
    agc1             (latitude, longitude, time, depth) float64 68.0 ... 80.0
    agc2             (latitude, longitude, time, depth) float64 76.0 ... 76.0
    agc3             (latitude, longitude, time, depth) float64 72.0 ... 86.0
    agc4             (latitude, longitude, time, depth) float64 70.0 ... 69.0
Attributes: (12/13)
    Latitude_DegMMddW:                      64 00.156 N
    Longitude_DegMMddN:                     167 56.043 W
    MooringID:                              20BSP-14A
    platform_deployment_date:               2020-09-18T07:37:00Z
    platform_deployment_cruise_name:        DY2012
    platform_recovery_date:                 2022-09-08T23:00:00Z
    ...                                     ...
    platform_deployment_recovery_comments:  
    WaterDepth:                             38.1
    date_created:                           2022-11-21T09:47:13Z
    date_modified:                          
    QC_indicator:                           unknown
    history:                                Trimmed to deployment.

xarray.Dataset

• Dimensions:
  • latitude: 1
  • longitude: 1
  • time: 34591
  • depth: 20
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2020-09-18T08:00:00 ... 2022-09-...

    array(['2020-09-18T08:00:00.000000000', '2020-09-18T08:30:00.000000000',
           '2020-09-18T09:00:00.000000000', ..., '2022-09-08T22:00:00.000000000',
           '2022-09-08T22:30:00.000000000', '2022-09-08T23:00:00.000000000'],
          dtype='datetime64[ns]')

  • depth
    (depth)
    float64
    -10.0 -8.0 -6.0 ... 24.0 26.0 28.0

    epic_key :

    D_3

    generic_name :

    depth

    units :

    meter

    long_name :

    depth below surface (meters)

    standard_name :

    depth

    positive :

    down

    array([-10.,  -8.,  -6.,  -4.,  -2.,  -0.,   2.,   4.,   6.,   8.,  10.,  12.,
            14.,  16.,  18.,  20.,  22.,  24.,  26.,  28.])

  • latitude
    (latitude)
    float64
    64.0

    epic_key :

    LON_501

    name :

    latitude

    generic_name :

    latitude

    units :

    degrees_north

    long_name :

    latitude

    standard_name :

    latitude

    array([64.0026])

  • longitude
    (longitude)
    float64
    -167.9

    epic_key :

    LAT_500

    name :

    longitude

    generic_name :

    longitude

    units :

    degrees_east

    long_name :

    longitude

    standard_name :

    longitude

    array([-167.93405])

• Data variables: (8)
  • u_curr_comp
    (latitude, longitude, time, depth)
    float64
    -6.515 -12.27 ... 0.5911 0.3259

    generic_name :

    u_curr_comp

    epic_key :

    u_1205

    units :

    cm s-1

    long_name :

    Zonal Current (Eastward) cm/s

    standard_name :

    eastward_sea_water_velocity

    array([[[[ -6.51539976, -12.27097898,   2.78746351, ...,  18.58069945,
               17.39438428,  16.95250986],
             [  8.91352581,  10.52413444,  37.87926701, ...,  19.13221822,
               19.20450893,  19.5903529 ],
             [ 10.331203  , -16.81628341,  -7.49759064, ...,  22.36794426,
               22.17787907,  22.7671917 ],
             ...,
             [         nan,          nan,          nan, ...,   0.52888931,
                0.1593056 ,   1.16368571],
             [         nan,          nan,          nan, ...,   1.27377029,
                1.25509348,   0.7648785 ],
             [         nan,          nan,          nan, ...,   0.18127944,
                0.59107357,   0.32586087]]]])

  • v_curr_comp
    (latitude, longitude, time, depth)
    float64
    -13.85 -34.77 ... 15.56 15.09

    generic_name :

    v_curr_comp

    epic_key :

    v_1206

    units :

    cm s-1

    long_name :

    Meridional Current (Northward) cm/s

    standard_name :

    northward_sea_water_velocity

    array([[[[-13.85169903, -34.76899589, -74.14263313, ..., -14.82422369,
              -13.1501101 , -12.68670207],
             [ 45.01143252,  30.0606486 ,  39.07545433, ..., -16.71520942,
              -16.92680822, -15.56624788],
             [-16.13028966, -13.16482481, -30.06636218, ..., -14.42827327,
              -13.59528154, -12.96861527],
             ...,
             [         nan,          nan,          nan, ...,  10.62498358,
               10.87679281,  10.84185573],
             [         nan,          nan,          nan, ...,  13.14790893,
               13.75589838,  13.11582864],
             [         nan,          nan,          nan, ...,  15.51570616,
               15.56118993,  15.09250856]]]])

  • w_curr_comp
    (latitude, longitude, time, depth)
    float64
    8.9 7.1 4.2 4.3 ... -0.2 -0.2 -0.1

    generic_name :

    w_curr_comp

    epic_key :

    w_1204

    units :

    cm s-1

    long_name :

    Vertical Current (Upward) cm/s

    standard_name :

    upward_sea_water_velocity

    array([[[[ 8.9,  7.1,  4.2, ...,  0.7,  0.6,  0.6],
             [18.9,  5.3, 10.7, ..., -0.5, -0.5, -0.5],
             [ 0.2,  1.2, -7.1, ..., -0.5, -0.2, -0.5],
             ...,
             [ nan,  nan,  nan, ...,  0.3,  0.1,  0.1],
             [ nan,  nan,  nan, ...,  0.1,  0. , -0.1],
             [ nan,  nan,  nan, ..., -0.2, -0.2, -0.1]]]])

  • w_curr_comp_err
    (latitude, longitude, time, depth)
    float64
    -3.6 0.0 36.4 ... -0.4 -0.1 0.4

    generic_name :

    w_curr_comp_err

    epic_key :

    Werr_1201

    units :

    cm s-1

    long_name :

    Vertical Current Error (Upward) cm/s

    standard_name :

    upward_sea_water_velocity_error

    array([[[[ -3.6,   0. ,  36.4, ...,   0.2,  -0.2,   0.4],
             [-18.6, -17.5,   6.4, ...,   1.4,   0. ,  -0.6],
             [-13.1,  -6.2, -13. , ...,   0.2,   0.1,   0.2],
             ...,
             [  nan,   nan,   nan, ...,  -0.4,  -0.6,   0.3],
             [  nan,   nan,   nan, ...,   0. ,   0.4,   0. ],
             [  nan,   nan,   nan, ...,  -0.4,  -0.1,   0.4]]]])

  • agc1
    (latitude, longitude, time, depth)
    float64
    68.0 78.0 91.0 ... 63.0 71.0 80.0

    standard_name :

    adcp_beam_echo_intensity

    generic_name :

    agc1

    epic_key :

    AGC1_1221

    units :

    counts

    longname :

    Echo Intensity (AGC) Beam 1

    array([[[[ 68.,  78.,  91., ..., 168., 167., 160.],
             [ 65.,  73.,  85., ..., 163., 160., 158.],
             [ 63.,  70.,  81., ..., 151., 154., 157.],
             ...,
             [ nan,  nan,  nan, ...,  62.,  71.,  80.],
             [ nan,  nan,  nan, ...,  63.,  71.,  80.],
             [ nan,  nan,  nan, ...,  63.,  71.,  80.]]]])

  • agc2
    (latitude, longitude, time, depth)
    float64
    76.0 86.0 98.0 ... 62.0 68.0 76.0

    standard_name :

    adcp_beam_echo_intensity

    generic_name :

    agc2

    epic_key :

    AGC2_1222

    units :

    counts

    longname :

    Echo Intensity (AGC) Beam 2

    array([[[[ 76.,  86.,  98., ..., 177., 177., 169.],
             [ 73.,  83.,  97., ..., 171., 169., 167.],
             [ 71.,  81.,  95., ..., 160., 164., 165.],
             ...,
             [ nan,  nan,  nan, ...,  62.,  69.,  78.],
             [ nan,  nan,  nan, ...,  62.,  69.,  78.],
             [ nan,  nan,  nan, ...,  62.,  68.,  76.]]]])

  • agc3
    (latitude, longitude, time, depth)
    float64
    72.0 81.0 92.0 ... 69.0 80.0 86.0

    standard_name :

    adcp_beam_echo_intensity

    generic_name :

    agc3

    epic_key :

    AGC3_1223

    units :

    counts

    longname :

    Echo Intensity (AGC) Beam 3

    array([[[[ 72.,  81.,  92., ..., 169., 169., 161.],
             [ 70.,  78.,  90., ..., 165., 164., 160.],
             [ 66.,  75.,  87., ..., 155., 158., 159.],
             ...,
             [ nan,  nan,  nan, ...,  68.,  78.,  86.],
             [ nan,  nan,  nan, ...,  69.,  79.,  87.],
             [ nan,  nan,  nan, ...,  69.,  80.,  86.]]]])

  • agc4
    (latitude, longitude, time, depth)
    float64
    70.0 79.0 90.0 ... 56.0 62.0 69.0

    standard_name :

    adcp_beam_echo_intensity

    generic_name :

    agc4

    epic_key :

    AGC4_1224

    units :

    counts

    longname :

    Echo Intensity (AGC) Beam 4

    array([[[[ 70.,  79.,  90., ..., 167., 167., 161.],
             [ 68.,  77.,  90., ..., 163., 161., 159.],
             [ 67.,  76.,  91., ..., 152., 156., 158.],
             ...,
             [ nan,  nan,  nan, ...,  56.,  62.,  70.],
             [ nan,  nan,  nan, ...,  56.,  62.,  70.],
             [ nan,  nan,  nan, ...,  56.,  62.,  69.]]]])

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-09-18 08:00:00', '2020-09-18 08:30:00',
                   '2020-09-18 09:00:00', '2020-09-18 09:30:00',
                   '2020-09-18 10:00:00', '2020-09-18 10:30:00',
                   '2020-09-18 11:00:00', '2020-09-18 11:30:00',
                   '2020-09-18 12:00:00', '2020-09-18 12:30:00',
                   ...
                   '2022-09-08 18:30:00', '2022-09-08 19:00:00',
                   '2022-09-08 19:30:00', '2022-09-08 20:00:00',
                   '2022-09-08 20:30:00', '2022-09-08 21:00:00',
                   '2022-09-08 21:30:00', '2022-09-08 22:00:00',
                   '2022-09-08 22:30:00', '2022-09-08 23:00:00'],
                  dtype='datetime64[ns]', name='time', length=34591, freq=None))

  • depth
    PandasIndex

    PandasIndex(Float64Index([-10.0,  -8.0,  -6.0,  -4.0,  -2.0,  -0.0,   2.0,   4.0,   6.0,
                    8.0,  10.0,  12.0,  14.0,  16.0,  18.0,  20.0,  22.0,  24.0,
                   26.0,  28.0],
                 dtype='float64', name='depth'))

  • latitude
    PandasIndex

    PandasIndex(Float64Index([64.0026], dtype='float64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Float64Index([-167.93405], dtype='float64', name='longitude'))

• Attributes: (13)

  Latitude_DegMMddW :

  64 00.156 N

  Longitude_DegMMddN :

  167 56.043 W

  MooringID :

  20BSP-14A

  platform_deployment_date :

  2020-09-18T07:37:00Z

  platform_deployment_cruise_name :

  DY2012

  platform_recovery_date :

  2022-09-08T23:00:00Z

  platform_recovery_cruise_name :

  AQ2201

  platform_deployment_recovery_comments :

  WaterDepth :

  38.1

  date_created :

  2022-11-21T09:47:13Z

  date_modified :

  QC_indicator :

  unknown

  history :

  Trimmed to deployment.

# xdf['history'] = xdf.history + 'QC of high u/v values'

#optional plotting

fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(24,8))
try:
    xdf['agc1'].T.plot(ax=axes[0,0], yincrease=False)
    xdf['agc2'].T.plot(ax=axes[0,1], yincrease=False)
    xdf['agc3'].T.plot(ax=axes[1,0], yincrease=False)
    xdf['agc4'].T.plot(ax=axes[1,1], yincrease=False)
except:
    pass

fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(24,8))
try:
    xdf['u_curr_comp'].T.plot(ax=axes[0,0],vmin=0, vmax=200, yincrease=False)
    xdf['v_curr_comp'].T.plot(ax=axes[0,1],vmin=0, vmax=200, yincrease=False)
    xdf['w_curr_comp'].T.plot(ax=axes[1,0],vmin=0, vmax=200, yincrease=False)
    xdf['w_curr_comp_err'].T.plot(ax=axes[1,1],vmin=0, vmax=200, yincrease=False)
except:
    pass

xdf.attrs['history'] = xdf.attrs['history']+'  removed U,V >100cm/s'

Trim above surface (negative values)

ixdf = xdf.where(xdf.depth >=-0.).dropna(how='all',dim='depth')

ixdf.attrs['history'] = ixdf.attrs['history']+'  trimmed above surface'

ixdf.to_netcdf(file_in.replace('.nc','.trimmed.nc'))

xdf = xa.load_dataset(file_in.replace('.nc','.trimmed.nc'))

ixdf = xdf.interpolate_na(dim='time',max_gap='2H')

ixdf.attrs['history'] = ixdf.attrs['history']+'  linearly interpolated 1hour gaps'

Interpolate over singleton missing gaps linearly

fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(24,8))
try:
    ixdf['agc1'].T.plot(ax=axes[0,0], yincrease=False)
    ixdf['agc2'].T.plot(ax=axes[0,1], yincrease=False)
    ixdf['agc3'].T.plot(ax=axes[1,0], yincrease=False)
    ixdf['agc4'].T.plot(ax=axes[1,1], yincrease=False)
except:
    pass

fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(24,8))
try:
    ixdf['u_curr_comp'].T.plot(ax=axes[0,0],vmin=0, vmax=200, yincrease=False)
    ixdf['v_curr_comp'].T.plot(ax=axes[0,1],vmin=0, vmax=200, yincrease=False)
    ixdf['w_curr_comp'].T.plot(ax=axes[1,0],vmin=0, vmax=200, yincrease=False)
    ixdf['w_curr_comp_err'].T.plot(ax=axes[1,1],vmin=0, vmax=200, yincrease=False)
except:
    pass

ixdf.to_netcdf(file_in.replace('.nc','.filtered.nc'))

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