In [ ]:

%pip install git+https://github.com/cioos-siooc/ocean-data-parser.git

%pip install folium
%pip install plotly

%pip install git+https://github.com/cioos-siooc/ocean-data-parser.git %pip install folium %pip install plotly

Install packages¶

In [ ]:

%pip install git+https://github.com/cioos-siooc/ocean-data-parser.git

%pip install folium
%pip install plotly

%pip install git+https://github.com/cioos-siooc/ocean-data-parser.git %pip install folium %pip install plotly

Imports¶

In [6]:

from ocean_data_parser.parsers import nmea
import pandas as pd
import xarray as xr
from glob import glob
from tqdm import tqdm
import requests
import folium

from ocean_data_parser.parsers import nmea import pandas as pd import xarray as xr from glob import glob from tqdm import tqdm import requests import folium

Download test data¶

NMEA test data is available here:

https://github.com/HakaiInstitute/ocean-data-parser/tree/main/tests/parsers_test_files/nmea

In [2]:

# retrieve raw data


def download_file_from_github(raw_url):
    """Download a github raw file localy"""
    file_path = raw_url.split("/")[-1]
    response = requests.get(raw_url)
    with open(file_path, "w") as f:
        f.write(response.text)
    return file_path


nmea_files = [
    "https://raw.githubusercontent.com/cioos-siooc/ocean-data-parser/development/tests/parsers_test_files/nmea/seaspan/nmeadata-2022-07-04_00.txt",
    "https://raw.githubusercontent.com/cioos-siooc/ocean-data-parser/development/tests/parsers_test_files/nmea/seaspan/nmeadata-2022-07-04_01.txt",
    "https://raw.githubusercontent.com/cioos-siooc/ocean-data-parser/development/tests/parsers_test_files/nmea/seaspan/nmeadata-2022-07-04_02.txt",
    "https://raw.githubusercontent.com/cioos-siooc/ocean-data-parser/development/tests/parsers_test_files/nmea/seaspan/nmeadata-2022-07-04_03.txt",
]

files = [download_file_from_github(file) for file in nmea_files]

# retrieve raw data def download_file_from_github(raw_url): """Download a github raw file localy""" file_path = raw_url.split("/")[-1] response = requests.get(raw_url) with open(file_path, "w") as f: f.write(response.text) return file_path nmea_files = [ "https://raw.githubusercontent.com/cioos-siooc/ocean-data-parser/development/tests/parsers_test_files/nmea/seaspan/nmeadata-2022-07-04_00.txt", "https://raw.githubusercontent.com/cioos-siooc/ocean-data-parser/development/tests/parsers_test_files/nmea/seaspan/nmeadata-2022-07-04_01.txt", "https://raw.githubusercontent.com/cioos-siooc/ocean-data-parser/development/tests/parsers_test_files/nmea/seaspan/nmeadata-2022-07-04_02.txt", "https://raw.githubusercontent.com/cioos-siooc/ocean-data-parser/development/tests/parsers_test_files/nmea/seaspan/nmeadata-2022-07-04_03.txt", ] files = [download_file_from_github(file) for file in nmea_files]

Load NMEA data¶

Here we load the different NMEA files and convert them a pandas dataframe.

In [50]:

nmea_files = glob("nmeadata-*.txt")
nmea_parsed = [
    nmea.file(file) for file in tqdm(nmea_files, desc="Import nmea files", unit="file")
]
# Aggregate all files
ds = xr.concat(nmea_parsed, dim="index")
ds

nmea_files = glob("nmeadata-*.txt") nmea_parsed = [ nmea.file(file) for file in tqdm(nmea_files, desc="Import nmea files", unit="file") ] # Aggregate all files ds = xr.concat(nmea_parsed, dim="index") ds

Import nmea files: 100%|██████████| 4/4 [00:00<00:00,  5.21file/s]

Out[50]:

<xarray.Dataset>
Dimensions:                                (index: 28800)
Coordinates:
  * index                                  (index) int64 0 1 2 ... 7198 7199
Data variables: (12/50)
    row                                    (index) float64 0.0 1.0 ... 7.199e+03
    prefix                                 (index) object '20220704 020001+00...
    talker                                 (index) object 'II' 'HC' ... 'WI'
    sentence_type                          (index) object 'MDA' 'HDT' ... 'MWV'
    subtype                                (index) object None None ... None
    manufacturer                           (index) object None None ... None
    ...                                     ...
    wind_angle                             (index) float64 nan nan ... nan 339.8
    reference                              (index) object None None ... None 'R'
    wind_speed                             (index) float64 nan nan ... nan 1.19
    wind_speed_units                       (index) object None None ... None 'N'
    wind_speed_relative_to_platform_knots  (index) float64 nan nan ... nan 1.19
    wind_direction_relative_to_platform    (index) float64 nan nan ... nan 339.8

xarray.Dataset

• Dimensions:
  • index: 28800
• Coordinates: (1)
  • index
    (index)
    int64
    0 1 2 3 4 ... 7196 7197 7198 7199

    array([   0,    1,    2, ..., 7197, 7198, 7199])

• Data variables: (50)
  • row
    (index)
    float64
    0.0 1.0 2.0 ... 7.198e+03 7.199e+03

    array([0.000e+00, 1.000e+00, 2.000e+00, ..., 7.197e+03, 7.198e+03,
           7.199e+03])

  • prefix
    (index)
    object
    '20220704 020001+00:00 ' ... '20...

    array(['20220704 020001+00:00 ', '20220704 020001+00:00 ',
           '20220704 020001+00:00 ', ..., '20220704 005959+00:00 ',
           '20220704 005959+00:00 ', '20220704 005959+00:00 '], dtype=object)

  • talker
    (index)
    object
    'II' 'HC' 'GP' ... 'HC' 'GP' 'WI'

    array(['II', 'HC', 'GP', ..., 'HC', 'GP', 'WI'], dtype=object)

  • sentence_type
    (index)
    object
    'MDA' 'HDT' 'RMC' ... 'RMC' 'MWV'

    array(['MDA', 'HDT', 'RMC', ..., 'HDT', 'RMC', 'MWV'], dtype=object)

  • subtype
    (index)
    object
    None None None ... None None None

    array([None, None, None, ..., None, None, None], dtype=object)

  • manufacturer
    (index)
    object
    None None None ... None None None

    array([None, None, None, ..., None, None, None], dtype=object)

  • b_pressure_inch
    (index)
    float64
    29.9 nan nan nan ... nan nan nan

    long_name :

    Barometric pressure, inches of mercury

    array([29.9,  nan,  nan, ...,  nan,  nan,  nan])

  • inches
    (index)
    object
    'I' None None ... None None None

    long_name :

    Inches

    array(['I', None, None, ..., None, None, None], dtype=object)

  • b_pressure_bar
    (index)
    float64
    1.011 nan nan nan ... nan nan nan

    long_name :

    Barometric pressure, bars

    array([1.011,   nan,   nan, ...,   nan,   nan,   nan])

  • bars
    (index)
    object
    'B' None None ... None None None

    long_name :

    Bars

    array(['B', None, None, ..., None, None, None], dtype=object)

  • air_temp
    (index)
    float64
    13.6 nan nan nan ... nan nan nan

    long_name :

    Air temperature, degrees C

    array([13.6,  nan,  nan, ...,  nan,  nan,  nan])

  • a_celsius
    (index)
    object
    'C' None None ... None None None

    long_name :

    Celsius

    array(['C', None, None, ..., None, None, None], dtype=object)

  • water_temp
    (index)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    Water temperature, degrees C

    array([nan, nan, nan, ..., nan, nan, nan])

  • w_celsius
    (index)
    object
    'C' None None ... None None None

    long_name :

    Celsius

    array(['C', None, None, ..., None, None, None], dtype=object)

  • rel_humidity
    (index)
    float64
    91.4 nan nan nan ... nan nan nan

    long_name :

    Relative humidity, percent

    array([91.4,  nan,  nan, ...,  nan,  nan,  nan])

  • abs_humidity
    (index)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    Absolute humidity, percent

    array([nan, nan, nan, ..., nan, nan, nan])

  • dew_point
    (index)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    Dew point, degrees C

    array([nan, nan, nan, ..., nan, nan, nan])

  • d_celsius
    (index)
    object
    'C' None None ... None None None

    long_name :

    Celsius

    array(['C', None, None, ..., None, None, None], dtype=object)

  • direction_true
    (index)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    Wind direction true

    array([nan, nan, nan, ..., nan, nan, nan])

  • true
    (index)
    object
    'T' None None ... None None None

    long_name :

    True

    array(['T', None, None, ..., None, None, None], dtype=object)

  • direction_magnetic
    (index)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    Wind direction magnetic

    array([nan, nan, nan, ..., nan, nan, nan])

  • magnetic
    (index)
    object
    'M' None None ... None None None

    long_name :

    Magnetic

    array(['M', None, None, ..., None, None, None], dtype=object)

  • wind_speed_knots
    (index)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    Wind speed knots

    array([nan, nan, nan, ..., nan, nan, nan])

  • knots
    (index)
    object
    'N' None None ... None None None

    long_name :

    Knots

    array(['N', None, None, ..., None, None, None], dtype=object)

  • wind_speed_meters
    (index)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    Wind speed meters/second

    array([nan, nan, nan, ..., nan, nan, nan])

  • meters
    (index)
    object
    'M' None None ... None None None

    long_name :

    Meters

    array(['M', None, None, ..., None, None, None], dtype=object)

  • heading
    (index)
    float64
    nan 146.9 nan nan ... 152.0 nan nan

    long_name :

    Heading

    array([  nan, 146.9,   nan, ..., 152. ,   nan,   nan])

  • hdg_true
    (index)
    object
    None 'T' None ... 'T' None None

    long_name :

    True

    array([None, 'T', None, ..., 'T', None, None], dtype=object)

  • timestamp
    (index)
    object
    None None ... '005954' None

    long_name :

    Timestamp

    array([None, None, '015956', ..., None, '005954', None], dtype=object)

  • status
    (index)
    object
    None None 'A' 'A' ... None 'A' 'A'

    long_name :

    Status

    array([None, None, 'A', ..., None, 'A', 'A'], dtype=object)

  • lat
    (index)
    object
    None None ... '4938.8998' None

    long_name :

    Latitude

    array([None, None, '4938.8896', ..., None, '4938.8998', None],
          dtype=object)

  • lat_dir
    (index)
    object
    None None 'N' ... None 'N' None

    long_name :

    Latitude Direction

    array([None, None, 'N', ..., None, 'N', None], dtype=object)

  • lon
    (index)
    object
    None None ... '12605.6196' None

    long_name :

    Longitude

    array([None, None, '12605.6298', ..., None, '12605.6196', None],
          dtype=object)

  • lon_dir
    (index)
    object
    None None 'W' ... None 'W' None

    long_name :

    Longitude Direction

    array([None, None, 'W', ..., None, 'W', None], dtype=object)

  • spd_over_grnd
    (index)
    float64
    nan nan 0.0 nan ... nan nan 0.1 nan

    long_name :

    Speed Over Ground

    array([nan, nan, 0. , ..., nan, 0.1, nan])

  • true_course
    (index)
    float64
    nan nan 38.0 nan ... nan 125.0 nan

    long_name :

    True Course

    array([ nan,  nan,  38., ...,  nan, 125.,  nan])

  • datestamp
    (index)
    object
    None None ... '040722' None

    long_name :

    Datestamp

    array([None, None, '040722', ..., None, '040722', None], dtype=object)

  • mag_variation
    (index)
    float64
    nan nan 12.2 nan ... nan 12.2 nan

    long_name :

    Magnetic Variation

    array([ nan,  nan, 12.2, ...,  nan, 12.2,  nan])

  • mag_var_dir
    (index)
    object
    None None 'E' ... None 'E' None

    long_name :

    Magnetic Variation Direction

    array([None, None, 'E', ..., None, 'E', None], dtype=object)

  • mode_indicator
    (index)
    object
    None None 'A' ... None 'A' None

    long_name :

    Mode Indicator

    array([None, None, 'A', ..., None, 'A', None], dtype=object)

  • nav_status
    (index)
    object
    None None 'V' ... None 'V' None

    long_name :

    Navigational Status

    array([None, None, 'V', ..., None, 'V', None], dtype=object)

  • latitude_degrees_north
    (index)
    float64
    nan nan 49.65 nan ... nan 49.65 nan

    long_name :

    Latitude

    array([     nan,      nan, 49.64816, ...,      nan, 49.64833,      nan])

  • longitude_degrees_east
    (index)
    float64
    nan nan -126.1 ... nan -126.1 nan

    long_name :

    Longitude

    array([       nan,        nan, -126.09383, ...,        nan, -126.09366,
                  nan])

  • gps_datetime
    (index)
    datetime64[ns]
    NaT NaT ... 2022-07-04T00:59:54 NaT

    long_name :

    GPS Time

    array([                          'NaT',                           'NaT',
           '2022-07-04T01:59:56.000000000', ...,
                                     'NaT', '2022-07-04T00:59:54.000000000',
                                     'NaT'], dtype='datetime64[ns]')

  • wind_angle
    (index)
    float64
    nan nan nan 279.6 ... nan nan 339.8

    long_name :

    Wind angle

    array([   nan,    nan,    nan, ...,    nan,    nan, 339.79])

  • reference
    (index)
    object
    None None None ... None None 'R'

    long_name :

    Reference

    array([None, None, None, ..., None, None, 'R'], dtype=object)

  • wind_speed
    (index)
    float64
    nan nan nan 4.2 ... nan nan 1.19

    long_name :

    Wind speed

    array([ nan,  nan,  nan, ...,  nan,  nan, 1.19])

  • wind_speed_units
    (index)
    object
    None None None ... None None 'N'

    long_name :

    Wind speed units

    array([None, None, None, ..., None, None, 'N'], dtype=object)

  • wind_speed_relative_to_platform_knots
    (index)
    float64
    nan nan nan 4.2 ... nan nan 1.19

    long_name :

    Wind Speed Relative To Platform [knots]

    array([ nan,  nan,  nan, ...,  nan,  nan, 1.19])

  • wind_direction_relative_to_platform
    (index)
    float64
    nan nan nan 279.6 ... nan nan 339.8

    long_name :

    Wind Direction Relative To Platform

    array([   nan,    nan,    nan, ...,    nan,    nan, 339.79])

• Indexes: (1)
  • index
    PandasIndex

    PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,
           ...
           7190, 7191, 7192, 7193, 7194, 7195, 7196, 7197, 7198, 7199],
          dtype='int64', name='index', length=28800))

• Attributes: (0)

Convert prefix of NMEA to datetime¶

In [66]:

# In this case the each rows prefix prior to the NMEA string is a timestamp, convert it to a datetime object
ds["computer_time"] = (
    ds["prefix"].dims,
    pd.Series(pd.to_datetime(ds["prefix"].str.strip(), format="%Y%m%d %H%M%S%z")),
)

# In this case the each rows prefix prior to the NMEA string is a timestamp, convert it to a datetime object ds["computer_time"] = ( ds["prefix"].dims, pd.Series(pd.to_datetime(ds["prefix"].str.strip(), format="%Y%m%d %H%M%S%z")), )

Reseample NMEA data to 2s¶

In [103]:

ds_resampled = (
    ds.swap_dims({"index": "computer_time"})
    .sortby("computer_time")
    .resample({"computer_time": "2s"})
    .first()
)
ds_resampled

ds_resampled = ( ds.swap_dims({"index": "computer_time"}) .sortby("computer_time") .resample({"computer_time": "2s"}) .first() ) ds_resampled

Out[103]:

<xarray.Dataset>
Dimensions:                                (computer_time: 7200)
Coordinates:
  * computer_time                          (computer_time) datetime64[ns] 202...
Data variables: (12/50)
    row                                    (computer_time) float64 0.0 ... 7....
    prefix                                 (computer_time) <U22 '20220704 000...
    talker                                 (computer_time) <U2 'II' ... 'II'
    sentence_type                          (computer_time) <U3 'MDA' ... 'MDA'
    subtype                                (computer_time) object None ... None
    manufacturer                           (computer_time) object None ... None
    ...                                     ...
    wind_angle                             (computer_time) float64 284.6 ... ...
    reference                              (computer_time) <U1 'R' 'R' ... 'R'
    wind_speed                             (computer_time) float64 2.68 ... 2...
    wind_speed_units                       (computer_time) <U1 'N' 'N' ... 'N'
    wind_speed_relative_to_platform_knots  (computer_time) float64 2.68 ... 2...
    wind_direction_relative_to_platform    (computer_time) float64 284.6 ... ...

xarray.Dataset

• Dimensions:
  • computer_time: 7200
• Coordinates: (1)
  • computer_time
    (computer_time)
    datetime64[ns]
    2022-07-04 ... 2022-07-04T03:59:58

    array(['2022-07-04T00:00:00.000000000', '2022-07-04T00:00:02.000000000',
           '2022-07-04T00:00:04.000000000', ..., '2022-07-04T03:59:54.000000000',
           '2022-07-04T03:59:56.000000000', '2022-07-04T03:59:58.000000000'],
          dtype='datetime64[ns]')

• Data variables: (50)
  • row
    (computer_time)
    float64
    0.0 4.0 8.0 ... 7.192e+03 7.196e+03

    array([0.000e+00, 4.000e+00, 8.000e+00, ..., 7.188e+03, 7.192e+03,
           7.196e+03])

  • prefix
    (computer_time)
    <U22
    '20220704 000001+00:00 ' ... '20...

    array(['20220704 000001+00:00 ', '20220704 000003+00:00 ',
           '20220704 000005+00:00 ', ..., '20220704 035955+00:00 ',
           '20220704 035957+00:00 ', '20220704 035959+00:00 '], dtype='<U22')

  • talker
    (computer_time)
    <U2
    'II' 'II' 'II' ... 'II' 'II' 'II'

    array(['II', 'II', 'II', ..., 'II', 'II', 'II'], dtype='<U2')

  • sentence_type
    (computer_time)
    <U3
    'MDA' 'MDA' 'MDA' ... 'MDA' 'MDA'

    array(['MDA', 'MDA', 'MDA', ..., 'MDA', 'MDA', 'MDA'], dtype='<U3')

  • subtype
    (computer_time)
    object
    None None None ... None None None

    array([None, None, None, ..., None, None, None], dtype=object)

  • manufacturer
    (computer_time)
    object
    None None None ... None None None

    array([None, None, None, ..., None, None, None], dtype=object)

  • b_pressure_inch
    (computer_time)
    float64
    29.8 29.8 29.8 ... 29.8 29.8 29.8

    long_name :

    Barometric pressure, inches of mercury

    array([29.8, 29.8, 29.8, ..., 29.8, 29.8, 29.8])

  • inches
    (computer_time)
    <U1
    'I' 'I' 'I' 'I' ... 'I' 'I' 'I' 'I'

    long_name :

    Inches

    array(['I', 'I', 'I', ..., 'I', 'I', 'I'], dtype='<U1')

  • b_pressure_bar
    (computer_time)
    float64
    1.01 1.01 1.01 ... 1.01 1.01 1.01

    long_name :

    Barometric pressure, bars

    array([1.01, 1.01, 1.01, ..., 1.01, 1.01, 1.01])

  • bars
    (computer_time)
    <U1
    'B' 'B' 'B' 'B' ... 'B' 'B' 'B' 'B'

    long_name :

    Bars

    array(['B', 'B', 'B', ..., 'B', 'B', 'B'], dtype='<U1')

  • air_temp
    (computer_time)
    float64
    14.3 14.3 14.3 ... 12.3 12.3 12.3

    long_name :

    Air temperature, degrees C

    array([14.3, 14.3, 14.3, ..., 12.3, 12.3, 12.3])

  • a_celsius
    (computer_time)
    <U1
    'C' 'C' 'C' 'C' ... 'C' 'C' 'C' 'C'

    long_name :

    Celsius

    array(['C', 'C', 'C', ..., 'C', 'C', 'C'], dtype='<U1')

  • water_temp
    (computer_time)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    Water temperature, degrees C

    array([nan, nan, nan, ..., nan, nan, nan])

  • w_celsius
    (computer_time)
    <U1
    'C' 'C' 'C' 'C' ... 'C' 'C' 'C' 'C'

    long_name :

    Celsius

    array(['C', 'C', 'C', ..., 'C', 'C', 'C'], dtype='<U1')

  • rel_humidity
    (computer_time)
    float64
    91.5 91.5 91.5 ... 94.1 94.1 94.1

    long_name :

    Relative humidity, percent

    array([91.5, 91.5, 91.5, ..., 94.1, 94.1, 94.1])

  • abs_humidity
    (computer_time)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    Absolute humidity, percent

    array([nan, nan, nan, ..., nan, nan, nan])

  • dew_point
    (computer_time)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    Dew point, degrees C

    array([nan, nan, nan, ..., nan, nan, nan])

  • d_celsius
    (computer_time)
    <U1
    'C' 'C' 'C' 'C' ... 'C' 'C' 'C' 'C'

    long_name :

    Celsius

    array(['C', 'C', 'C', ..., 'C', 'C', 'C'], dtype='<U1')

  • direction_true
    (computer_time)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    Wind direction true

    array([nan, nan, nan, ..., nan, nan, nan])

  • true
    (computer_time)
    <U1
    'T' 'T' 'T' 'T' ... 'T' 'T' 'T' 'T'

    long_name :

    True

    array(['T', 'T', 'T', ..., 'T', 'T', 'T'], dtype='<U1')

  • direction_magnetic
    (computer_time)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    Wind direction magnetic

    array([nan, nan, nan, ..., nan, nan, nan])

  • magnetic
    (computer_time)
    <U1
    'M' 'M' 'M' 'M' ... 'M' 'M' 'M' 'M'

    long_name :

    Magnetic

    array(['M', 'M', 'M', ..., 'M', 'M', 'M'], dtype='<U1')

  • wind_speed_knots
    (computer_time)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    Wind speed knots

    array([nan, nan, nan, ..., nan, nan, nan])

  • knots
    (computer_time)
    <U1
    'N' 'N' 'N' 'N' ... 'N' 'N' 'N' 'N'

    long_name :

    Knots

    array(['N', 'N', 'N', ..., 'N', 'N', 'N'], dtype='<U1')

  • wind_speed_meters
    (computer_time)
    float64
    nan nan nan nan ... nan nan nan nan

    long_name :

    Wind speed meters/second

    array([nan, nan, nan, ..., nan, nan, nan])

  • meters
    (computer_time)
    <U1
    'M' 'M' 'M' 'M' ... 'M' 'M' 'M' 'M'

    long_name :

    Meters

    array(['M', 'M', 'M', ..., 'M', 'M', 'M'], dtype='<U1')

  • heading
    (computer_time)
    float64
    157.3 157.3 157.4 ... 257.3 257.3

    long_name :

    Heading

    array([157.3, 157.3, 157.4, ..., 257.3, 257.3, 257.3])

  • hdg_true
    (computer_time)
    <U1
    'T' 'T' 'T' 'T' ... 'T' 'T' 'T' 'T'

    long_name :

    True

    array(['T', 'T', 'T', ..., 'T', 'T', 'T'], dtype='<U1')

  • timestamp
    (computer_time)
    <U6
    '235956' '235958' ... '035954'

    long_name :

    Timestamp

    array(['235956', '235958', '000000', ..., '035950', '035952', '035954'],
          dtype='<U6')

  • status
    (computer_time)
    <U1
    'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'

    long_name :

    Status

    array(['A', 'A', 'A', ..., 'A', 'A', 'A'], dtype='<U1')

  • lat
    (computer_time)
    <U9
    '4938.8998' ... '4938.8098'

    long_name :

    Latitude

    array(['4938.8998', '4938.8998', '4938.8998', ..., '4938.8098',
           '4938.8098', '4938.8098'], dtype='<U9')

  • lat_dir
    (computer_time)
    <U1
    'N' 'N' 'N' 'N' ... 'N' 'N' 'N' 'N'

    long_name :

    Latitude Direction

    array(['N', 'N', 'N', ..., 'N', 'N', 'N'], dtype='<U1')

  • lon
    (computer_time)
    <U10
    '12605.6100' ... '12616.5696'

    long_name :

    Longitude

    array(['12605.6100', '12605.6100', '12605.6100', ..., '12616.5498',
           '12616.5600', '12616.5696'], dtype='<U10')

  • lon_dir
    (computer_time)
    <U1
    'W' 'W' 'W' 'W' ... 'W' 'W' 'W' 'W'

    long_name :

    Longitude Direction

    array(['W', 'W', 'W', ..., 'W', 'W', 'W'], dtype='<U1')

  • spd_over_grnd
    (computer_time)
    float64
    0.1 0.1 0.1 0.1 ... 9.29 9.29 9.29

    long_name :

    Speed Over Ground

    array([0.1 , 0.1 , 0.1 , ..., 9.29, 9.29, 9.29])

  • true_course
    (computer_time)
    float64
    323.0 316.0 309.0 ... 260.0 260.0

    long_name :

    True Course

    array([323., 316., 309., ..., 260., 260., 260.])

  • datestamp
    (computer_time)
    <U6
    '030722' '030722' ... '040722'

    long_name :

    Datestamp

    array(['030722', '030722', '040722', ..., '040722', '040722', '040722'],
          dtype='<U6')

  • mag_variation
    (computer_time)
    float64
    12.2 12.2 12.2 ... 12.3 12.3 12.3

    long_name :

    Magnetic Variation

    array([12.2, 12.2, 12.2, ..., 12.3, 12.3, 12.3])

  • mag_var_dir
    (computer_time)
    <U1
    'E' 'E' 'E' 'E' ... 'E' 'E' 'E' 'E'

    long_name :

    Magnetic Variation Direction

    array(['E', 'E', 'E', ..., 'E', 'E', 'E'], dtype='<U1')

  • mode_indicator
    (computer_time)
    <U1
    'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'

    long_name :

    Mode Indicator

    array(['A', 'A', 'A', ..., 'A', 'A', 'A'], dtype='<U1')

  • nav_status
    (computer_time)
    <U1
    'V' 'V' 'V' 'V' ... 'V' 'V' 'V' 'V'

    long_name :

    Navigational Status

    array(['V', 'V', 'V', ..., 'V', 'V', 'V'], dtype='<U1')

  • latitude_degrees_north
    (computer_time)
    float64
    49.65 49.65 49.65 ... 49.65 49.65

    long_name :

    Latitude

    array([49.64833, 49.64833, 49.64833, ..., 49.64683, 49.64683, 49.64683])

  • longitude_degrees_east
    (computer_time)
    float64
    -126.1 -126.1 ... -126.3 -126.3

    long_name :

    Longitude

    array([-126.0935 , -126.0935 , -126.0935 , ..., -126.27583, -126.276  ,
           -126.27616])

  • gps_datetime
    (computer_time)
    datetime64[ns]
    2022-07-03T23:59:56 ... 2022-07-...

    long_name :

    GPS Time

    array(['2022-07-03T23:59:56.000000000', '2022-07-03T23:59:58.000000000',
           '2022-07-04T00:00:00.000000000', ...,
           '2022-07-04T03:59:50.000000000', '2022-07-04T03:59:52.000000000',
           '2022-07-04T03:59:54.000000000'], dtype='datetime64[ns]')

  • wind_angle
    (computer_time)
    float64
    284.6 286.1 293.9 ... 346.7 348.4

    long_name :

    Wind angle

    array([284.59, 286.1 , 293.89, ..., 348.49, 346.69, 348.39])

  • reference
    (computer_time)
    <U1
    'R' 'R' 'R' 'R' ... 'R' 'R' 'R' 'R'

    long_name :

    Reference

    array(['R', 'R', 'R', ..., 'R', 'R', 'R'], dtype='<U1')

  • wind_speed
    (computer_time)
    float64
    2.68 3.89 3.38 ... 21.29 20.78

    long_name :

    Wind speed

    array([ 2.68,  3.89,  3.38, ..., 22.08, 21.29, 20.78])

  • wind_speed_units
    (computer_time)
    <U1
    'N' 'N' 'N' 'N' ... 'N' 'N' 'N' 'N'

    long_name :

    Wind speed units

    array(['N', 'N', 'N', ..., 'N', 'N', 'N'], dtype='<U1')

  • wind_speed_relative_to_platform_knots
    (computer_time)
    float64
    2.68 3.89 3.38 ... 21.29 20.78

    long_name :

    Wind Speed Relative To Platform [knots]

    array([ 2.68,  3.89,  3.38, ..., 22.08, 21.29, 20.78])

  • wind_direction_relative_to_platform
    (computer_time)
    float64
    284.6 286.1 293.9 ... 346.7 348.4

    long_name :

    Wind Direction Relative To Platform

    array([284.59, 286.1 , 293.89, ..., 348.49, 346.69, 348.39])

• Indexes: (1)
  • computer_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2022-07-04 00:00:00', '2022-07-04 00:00:02',
                   '2022-07-04 00:00:04', '2022-07-04 00:00:06',
                   '2022-07-04 00:00:08', '2022-07-04 00:00:10',
                   '2022-07-04 00:00:12', '2022-07-04 00:00:14',
                   '2022-07-04 00:00:16', '2022-07-04 00:00:18',
                   ...
                   '2022-07-04 03:59:40', '2022-07-04 03:59:42',
                   '2022-07-04 03:59:44', '2022-07-04 03:59:46',
                   '2022-07-04 03:59:48', '2022-07-04 03:59:50',
                   '2022-07-04 03:59:52', '2022-07-04 03:59:54',
                   '2022-07-04 03:59:56', '2022-07-04 03:59:58'],
                  dtype='datetime64[ns]', name='computer_time', length=7200, freq='2S'))

• Attributes: (0)

Plot Resulting Data¶

In [102]:

# Plot data
ds_resampled[
    [
        "air_temp",
        "wind_speed_relative_to_platform_knots",
        "spd_over_grnd",
    ]
].to_dataframe().plot(xlabel="time", figsize=(16, 8), subplots=True)

# Plot data ds_resampled[ [ "air_temp", "wind_speed_relative_to_platform_knots", "spd_over_grnd", ] ].to_dataframe().plot(xlabel="time", figsize=(16, 8), subplots=True)

Out[102]:

array([<Axes: xlabel='time'>, <Axes: xlabel='time'>,
       <Axes: xlabel='time'>], dtype=object)

Plot position on a map¶

In [101]:

# Plot lat/lon on a map
lat, lon = "latitude_degrees_north", "longitude_degrees_east"
df = ds_resampled.to_dataframe()
map = folium.Map(location=df[[lat, lon]].mean())
folium.PolyLine(df[[lat, lon]].dropna().values, color="red").add_to(map)
map

# Plot lat/lon on a map lat, lon = "latitude_degrees_north", "longitude_degrees_east" df = ds_resampled.to_dataframe() map = folium.Map(location=df[[lat, lon]].mean()) folium.PolyLine(df[[lat, lon]].dropna().values, color="red").add_to(map) map

Out[101]:

Make this Notebook Trusted to load map: File -> Trust Notebook

In [ ]: