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 glob import glob

import folium
import pandas as pd
import requests
import xarray as xr
from tqdm import tqdm

from ocean_data_parser.parsers import nmea

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

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 locally."""
    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 locally.""" 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 [ ]: