From 2e2d9a8da6335b8c79c580e02907efd24949c623 Mon Sep 17 00:00:00 2001 From: Luke Marsden Date: Fri, 19 Apr 2024 10:55:23 +0200 Subject: [PATCH] Update documentation --- 01_opening_and_understanding.html | 11 +- 02_creating_plots.html | 47 +- 03_extracting_data_to_different_formats.html | 75 +-- 04_creating_a_cfnetcdf_file.html | 277 +++++------ ...o_structure_your_NetCDF_file_or_files.html | 265 +++++------ ...ing_multiple_cfnetcdf_files_in_one_go.html | 11 +- ...mbing_data_from_multiple_netcdf_files.html | 17 +- 08_ancillary_variables.html | 41 +- 09_cells_and_cell_methods.html | 371 ++++----------- ...extracting_data_to_different_formats.ipynb | 2 +- _sources/04_creating_a_cfnetcdf_file.ipynb | 6 +- _sources/08_ancillary_variables.ipynb | 18 +- _sources/09_cells_and_cell_methods.ipynb | 433 ++++-------------- genindex.html | 1 + intro.html | 2 + objects.inv | Bin 600 -> 574 bytes search.html | 1 + searchindex.js | 2 +- 18 files changed, 564 insertions(+), 1016 deletions(-) diff --git a/01_opening_and_understanding.html b/01_opening_and_understanding.html index 479bba3..28b0e54 100644 --- a/01_opening_and_understanding.html +++ b/01_opening_and_understanding.html @@ -176,6 +176,7 @@
  • 06: Creating multiple CF-NetCDF files in one go
  • 07: Combining data from multiple netcdf files
  • 08: Ancillary variables
  • +
  • 09: Cells and cell methods
  • @@ -822,10 +823,10 @@

    Importing some data from OPeNDAP -
    @@ -808,12 +809,12 @@

    Opening a file -
    [<matplotlib.lines.Line2D at 0x764db7e8b6d0>]
    +
    [<matplotlib.lines.Line2D at 0x720782e06790>]
     
    _images/700b3601089df91995112e597e811c6917990efa6157bda31a3d2ec12ed71650.png @@ -864,7 +865,7 @@

    Variables with one dimension -
    [<matplotlib.lines.Line2D at 0x764dbc483890>]
    +
    [<matplotlib.lines.Line2D at 0x72078237a410>]
     
    _images/c0ec2de1791af233d58c94b4a3faa72a7b9fb73b20d080755898175452905fa5.png @@ -878,7 +879,7 @@

    Variables with one dimension -
    [<matplotlib.lines.Line2D at 0x764db7513550>]
    +
    [<matplotlib.lines.Line2D at 0x7207823f5990>]
     
    _images/5c2bd4bb90b78536cb99cb13e81d6ba6b448b1c2fff77671e712640c6bb57e1a.png @@ -892,7 +893,7 @@

    Variables with one dimension -
    <matplotlib.collections.PathCollection at 0x764db7d2c750>
    +
    <matplotlib.collections.PathCollection at 0x720782cae650>
     
    _images/b4546a355348d1ec97e7adf35fb868e29d0b6fe4383712f4a332197583675b06.png @@ -905,7 +906,7 @@

    Variables with one dimension -
    <matplotlib.collections.PathCollection at 0x764db7dcc750>
    +
    <matplotlib.collections.PathCollection at 0x720782cc2a50>
     
    _images/e6476b144a06386d7b2d62dd220fe331a28ee06c0f635bcd53b021f7eb9c4275.png @@ -938,7 +939,7 @@

    Variables with one dimension -
    [<matplotlib.lines.Line2D at 0x764db7291490>]
    +
    [<matplotlib.lines.Line2D at 0x720782061d10>]
     
    _images/fddc15bf159581ae91d5c2412d71b38c8b4b038568b5dce9a7ee981c3e39c3a0.png @@ -953,7 +954,7 @@

    Variables with one dimension -
    [<matplotlib.lines.Line2D at 0x764db7db9950>]
    +
    [<matplotlib.lines.Line2D at 0x720782ff4610>]
     
    _images/e3ad651fe2b82ac3051b48aee78ca4db5762a1402babe9f75a275f241ab4891f.png @@ -1401,31 +1402,31 @@

    2D plots - e.g. maps @@ -1830,21 +1831,21 @@

    2D plots - e.g. maps @@ -1862,7 +1863,7 @@

    2D plots - e.g. maps - @@ -811,7 +812,7 @@

    Initialising your xarray dataset object

    + *empty*

    The object has a dimension of length 10 and a coordinate variable (time) which has a dimension of time. The values are all integers. Some more examples.

    @@ -1656,12 +1657,12 @@

    Your times are timestamps @@ -2056,7 +2057,7 @@

    We have multiple dimensions @@ -2460,9 +2461,9 @@

    2D array, e.g. a grid of latitude and longitudes -
    array([[4, 6],
    -       [9, 2],
    -       [9, 2]])
    +
    array([[3, 7],
    +       [9, 1],
    +       [2, 9]])
     
    @@ -2846,9 +2847,9 @@

    2D array, e.g. a grid of latitude and longitudes @@ -3222,14 +3223,14 @@

    2D array, e.g. a grid of latitude and longitudes @@ -3695,210 +3696,210 @@

    3D data from pandas dataframe to 3D grid -
    array([[[33.62293383, 32.18699116],
    -        [30.65977827, 33.96280268],
    -        [32.97305267, 31.82980051]],
    +
    array([[[34.74944745, 32.1917588 ],
    +        [31.74072064, 32.34019024],
    +        [30.60560313, 34.64472006]],
     
    -       [[30.14290084, 33.5186714 ],
    -        [31.80274767, 30.52528155],
    -        [30.09057322, 32.71881746]],
    +       [[31.03317072, 34.48681636],
    +        [32.62240039, 34.45982316],
    +        [30.75528895, 31.82979994]],
     
    -       [[34.33219077, 33.50817531],
    -        [34.1069542 , 34.34160461],
    -        [30.93894806, 31.7712962 ]],
    +       [[33.14207508, 33.70822007],
    +        [31.71495988, 31.15323774],
    +        [32.11855695, 33.5303625 ]],
     
    -       [[32.55988121, 33.60437791],
    -        [34.68379801, 30.23804545],
    -        [32.08238447, 30.74944872]],
    +       [[34.64192769, 30.39055312],
    +        [34.7657189 , 30.11090229],
    +        [31.5641614 , 32.33688624]],
     
    -       [[31.78173197, 32.32044399],
    -        [32.40007812, 33.44888098],
    -        [33.7524908 , 32.82253346]]])
    +       [[34.78117549, 32.48440525],
    +        [32.34804671, 33.12654485],
    +        [31.08760317, 31.05419004]]])
     
    @@ -4316,36 +4317,36 @@

    3D data from pandas dataframe to 3D grid
    @@ -5175,36 +5176,36 @@

    Variable attributes @@ -5212,7 +5213,7 @@

    Global attributeshttps://adc.met.no/node/4

    And remember we might want to select additional applicable global attributes from this section of the CF conventions: https://cfconventions.org/Data/cf-conventions/cf-conventions-1.11/cf-conventions.html#attribute-appendix

    -

    Go through and add each required attribute and any others you wish to. You are also welcome to add any global attributes from the CF conventions as well as any custom attributes on top of these requirements. Similarly to variable attributes, this can either be done one by one or all in one in a dictionary.

    +

    Go through and add each required attribute and any others you wish to. You are also welcome to add any custom attributes on top of these requirements. Similarly to variable attributes, this can either be done one by one or all in one in a dictionary.

    xrds.attrs['title'] = 'my title' # One by one
    @@ -5244,7 +5245,7 @@ 

    Global attributes @@ -827,10 +828,10 @@

    Introducing the data + dtype='float32', name='PRES', length=259))

  • qc_manual :
    Recommendations for in-situ data Near Real Time Quality Control https://doi.org/10.13155/36230
    contact :
    datahjelp@hi.no
    distribution_statement :
    These data are public and free of charge. User assumes all risk for use of data. User must display citation in any publication or product using data. User must contact PI prior to any commercial use of data.
    naming_authority :
    no.unis
    license :
    https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
    data_assembly_center :
    IMR
    update_interval :
    void
    area :
    Arctic Ocean
    author :
    Conventions :
    CF-1.8, ACDD-1.3, OceanSITES Manual 1.4
    data_mode :
    M
    comment :
    Descending CTD profile
    history :
    Created at 2022-08-08T12:43:51Z using the xarray library in Python
    netcdf_version :
    netCDF-4 classic model
    quality_index :
    A
    quality_control_indicator :
    0
    publisher_name :
    Elisabeth Jones
    publisher_email :
    datahjelp@imr.no
    wmo_platform_code :
    ices_platform_code :
    site_code :
    bottom_depth :
    wmo_inst_type :
    geospatial_vertical_positive :
    down
    platform_code :
    58US
    platform_name :
    Kronprins Haakon
    source :
    research vessel
    institution_country :
    Norway
    source_platform_category_code :
    31
    data_type :
    OceanSITES vertical profile
    cdm_data_type :
    PROFILE
    instrument :
    CTD Seabird
    keywords :
    EARTH SCIENCE > OCEANS > OCEAN OPTICS > FLUORESCENCE, EARTH SCIENCE > OCEANS > SALINITY/DENSITY > DENSITY, EARTH SCIENCE > OCEANS > SALINITY/DENSITY > OCEAN SALINITY, EARTH SCIENCE > OCEANS > OCEAN ACOUSTICS > ACOUSTIC VELOCITY, EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > WATER TEMPERATURE, EARTH SCIENCE > OCEANS > SALINITY/DENSITY > CONDUCTIVITY
    keywords_vocabulary :
    GCMD:GCMD Keywords
    id :
    NMDC_Nansen-Legacy_PR_CT_58US_2021708_ISG/SVR1
    time_coverage_start :
    2021-07-12T19:05:04Z
    time_coverage_end :
    2021-07-12T19:05:04Z
    date_update :
    2022-08-08T12:43:51Z
    date_created :
    2022-08-08T12:43:51Z
    geospatial_lat_min :
    78.1282
    geospatial_lat_max :
    78.1282
    geospatial_lon_min :
    14.0032
    geospatial_lon_max :
    14.0032
    geospatial_vertical_min :
    2.0
    geospatial_vertical_max :
    261.0
    citation :
    These data were collected and made freely available by the Nansen Legacy project and the programs that contribute to it.
    project :
    The Nansen Legacy Project (RCN # 276730)
    references :
    https://doi.org/10.21335/NMDC-2085836005
    featureType :
    Profile
    summary :
    The Joint Cruise 2-1 addressed objectives of the work packages Physical drivers (Research Foci 1), Human impact (Research Foci 2) and The living Barents Sea (Research Foci 3) along the Nansen Legacy transect in open water and within the sea ice. The cruise focussed on comparing the state of the physical, chemical and biological conditions in the southern and northern parts of the study area. These data are created from the CTD data published by NMDC for the whole cruise (https://doi.org/10.21335/NMDC-2085836005). The values have not be changed.
    doi :
    10.21335/NMDC-2085836005-ISG_SVR1
    pi_name :
    Janne Søreide
    institution :
    Institute of Marine Research
    institution_edmo_code :
    1351
    institution_references :
    https://www.hi.no/hi
    title :
    CTD_station_ISG_SVR1_-_Nansen_Legacy_Cruise_-_2021_Joint_Cruise_2-1
    creator_institution :
    Institute of Marine Research
    creator_name :
    Elisabeth Jones
    creator_type :
    person
    creator_email :
    datahjelp@imr.no
    creator_url :
    https://www.hi.no/hi
    publisher :
    The Norwegian Marine Data Centre (NMD)
    publisher_url :
    https://www.hi.no/hi
    acknowledgement :
    The Nansen Legacy project is funded by the Research Council of Norway (RCN # 276730). These data are created from the CTD data published by NMDC for the whole cruise (10.21335/NMDC-2085836005). The values have not be changed. For information about this process, please contact Luke Marsden at data.nleg@unis.no
    cruiseNumber :
    2021708
    cruiseName :
    2021 Joint Cruise 2-1
    geospatial_vertical_units :
    dbar
    geospatial_vertical_resolution :
    1 dbar
    pi_email :
    jannes@unis.no
    pi_institution :
    University Centre in Svalbard
    station_name :
    ISG/SVR1
    metadata_link :
    https://doi.org/10.21335/NMDC-2085836005-ISG_SVR1
    _NCProperties :
    version=2,netcdf=4.6.3,hdf5=1.10.5
  • Let’s look at a quick example of how we can extract the data into numpy arrays

    -
    2021-07-16T21:18:06Z
    -
    -
    -
    2021-07-16T22:07:07Z
    -
    -
    -
    2021-07-16T23:09:27Z
    -
    -
    -
    2021-07-16T23:56:54Z
    -
    -

    diff --git a/08_ancillary_variables.html b/08_ancillary_variables.html index c1616eb..9f897d8 100644 --- a/08_ancillary_variables.html +++ b/08_ancillary_variables.html @@ -65,6 +65,7 @@ + @@ -175,6 +176,7 @@
  • 06: Creating multiple CF-NetCDF files in one go
  • 07: Combining data from multiple netcdf files
  • 08: Ancillary variables
  • +
  • 09: Cells and cell methods
  • @@ -799,7 +801,7 @@

    Basic example without ancillary data @@ -807,7 +809,7 @@

    Assigning quality or status flagshttps://cfconventions.org/Data/cf-conventions/cf-conventions-1.11/cf-conventions.html#ancillary-data

    We need to create a new variable for the flags.

    -

    Flags are stored as numbers, and with the meanings for the numbers stored as a variable attribute. The flag_meanings should be separated by spaces - so don’t include spaces in any of the terms you use! The length of the flag values and flag_meanings should be the same.

    +

    Flags are stored as numbers, and with the meanings for the numbers stored as a variable attribute. The flag_meanings should be separated by spaces - so don’t include spaces in any of the terms you use! The length of the flag values and flag_meanings should be the same.

    chla_flag_possible_values = [0,1,2,3,4,5,6,7,8,9]
    @@ -816,7 +818,7 @@ 

    Assigning quality or status flagshttp://www.oceansites.org/docs/oceansites_data_format_reference_manual.pdf

    However, other quality flag conventions exist.

    @@ -1201,9 +1203,9 @@

    Assigning quality or status flags
    xrds['Chlorophyll_A'].attrs['ancillary_variables'] = "Chlorophyll_A_quality_flags"
    @@ -1211,9 +1213,9 @@ 

    Assigning quality or status flagsstandard_name for you. +

    Finally we need to add our metadata to the ancillary variable to describe it. There are a lot of standard names for different types of flags. Search for flag here to find a suitable standard_name for you. https://cfconventions.org/Data/cf-standard-names/current/build/cf-standard-name-table.html

    -

    The CF conventions also has standardised variable attributes you can use for flag_valuesand flag_meanings. You often see the valid_range attribute used here too to explicitely show that any values outside of that range are invalid. You could use valid_min and valid_max used instead.

    +

    The CF conventions also has standardised variable attributes you can use for flag_values and flag_meanings. You often see the valid_range attribute used here too to explicitely show that any values outside of that range are invalid. You could use valid_min and valid_max used instead.

    # Metadata for the 'Chlorophyll_A_quality_flags' variable
    @@ -1606,9 +1608,9 @@ 

    Assigning quality or status flagsConventions global attribute, for example

    +

    Make sure you refer to the conventions you are following for your quality flags in your Conventions global attribute, for example

    xrds.attrs['Conventions'] = 'CF-1.8, ACDD-1.3, OceanSITES Manual 1.4'
    @@ -1988,12 +1990,12 @@ 

    Assigning quality or status flags

    Retrieving only good_quality data#

    -

    Suppose we want to retrieve only the good_quality Chlorophyll_A data, where Chlorophyll_A_quality_flags = 1

    +

    Suppose we want to retrieve only the good_quality Chlorophyll_A data, where Chlorophyll_A_quality_flags = 1

    good_quality_chlorophyll_a = xrds['Chlorophyll_A'].where(xrds['Chlorophyll_A_quality_flags'] == 1)
    @@ -2374,7 +2376,7 @@ 

    Retrieving only good_quality data @@ -2757,7 +2759,7 @@

    Retrieving only good_quality data @@ -3155,9 +3157,9 @@

    Other ancillary data

    + Conventions: CF-1.8, ACDD-1.3, OceanSITES Manual 1.4

    -

    More work needs to be done to expand the CF conventions to standardise ancillary data. At the time of writing, a standard_name for the volume of sea water filtered does not exist.

    +

    More work needs to be done to expand the CF conventions to standardise ancillary data. At the time of writing, a standard_name for the volume of sea water filtered does not exist.

    This is where the scientific community can help!

    New standard names can be suggested by raising an issue of this GitHub repository: cf-convention/discuss#issues

    @@ -3205,6 +3207,15 @@

    Other ancillary data07: Combining data from multiple netcdf files

    + +
    +

    next

    +

    09: Cells and cell methods

    +
    + +
    diff --git a/09_cells_and_cell_methods.html b/09_cells_and_cell_methods.html index 63c8b44..e3efb19 100644 --- a/09_cells_and_cell_methods.html +++ b/09_cells_and_cell_methods.html @@ -65,6 +65,7 @@ + @@ -165,7 +166,7 @@ -
    @@ -369,7 +371,6 @@

    Contents

    @@ -393,22 +394,24 @@

    09: Cells and cell methods

    Maximum monthly temperatures#

    Let’s start with the basic code to create our xarray object for a time series of temperature values.

    -

    Months and years not recommended in CF because of potential for confusion with leap years. Therefore, we can use days since 1970-01-01 (the epoch) for first day of each month for 2023.

    +

    Units of months since date or years since date are not recommended in CF because of potential for confusion with leap years. Therefore, we can use days since 1970-01-01 (the epoch) for first day of each month for 2023.

    import datetime
    +import xarray as xr
    +import calendar
    +import numpy as np
     
    -# Function to calculate days since 1970-01-01 for the start of each month in a year
    -def get_days_since_epoch(year):
    -    days_since_epoch = []
    -    for month in range(1, 13):
    -        first_day_of_month = datetime.date(year, month, 1)
    -        days_since_epoch.append((first_day_of_month - datetime.date(1970, 1, 1)).days)
    -    return days_since_epoch
    -
    -# Get days since 1970-01-01 for the start of each month in 2023
    -days_since_epoch_2023 = get_days_since_epoch(2023)
    -print(days_since_epoch_2023)
    +days_since_epoch_month_start = []
    +year = 2023
    +epoch = datetime.date(1970, 1, 1)
    +
    +for month in range(1, 13):
    +    first_day_of_month = datetime.date(year, month, 1)
    +    time_difference = (first_day_of_month - epoch).days
    +    days_since_epoch_month_start.append(time_difference)
    +
    +print(days_since_epoch_month_start)
     
    @@ -420,14 +423,11 @@

    Maximum monthly temperatures
    -
    import xarray as xr
    -
    -days_since_epoch = [19358, 19389, 19417, 19448, 19478, 19509, 19539, 19570, 19601, 19631, 19662, 19692]
    -maximum_monthly_temperatures = [4.6,5.2,7.1,12.3,17.8,21.3,24.6,22.8,19.0,14.2,8.8,6.1]
    +
    maximum_monthly_temperatures = [4.6,5.2,7.1,12.3,17.8,21.3,24.6,22.8,19.0,14.2,8.8,6.1]
     
     xrds = xr.Dataset(
         coords={
    -        'time': days_since_epoch
    +        'time': days_since_epoch_month_start
         },
         data_vars={
             'Temperature': ('time', maximum_monthly_temperatures)
    @@ -819,197 +819,63 @@ 

    Maximum monthly temperatures
    -
    import numpy as np
    -time_bounds_2d_array = np.array([
    -    [738521, 738551],
    -    [738552, 738579],
    -    [738580, 738610],
    -    [738611, 738640],
    -    [738641, 738671],
    -    [738672, 738701],
    -    [738702, 738732],
    -    [738733, 738763],
    -    [738764, 738793],
    -    [738794, 738824],
    -    [738825, 738854],
    -    [738855, 738885]
    -])
    -time_bounds_2d_array
    +
    days_since_epoch_month_end = []
    +
    +for month in range(1, 13):
    +    # Find the last day of the month
    +    last_day_of_month = calendar.monthrange(year, month)[1]
    +    last_day_date = datetime.date(year, month, last_day_of_month)
    +    
    +    # Calculate the days since epoch for the last day of the month
    +    time_difference = (last_day_date - epoch).days
    +    days_since_epoch_month_end.append(time_difference)
    +
    +print(days_since_epoch_month_end)
     
    -
    array([[738521, 738551],
    -       [738552, 738579],
    -       [738580, 738610],
    -       [738611, 738640],
    -       [738641, 738671],
    -       [738672, 738701],
    -       [738702, 738732],
    -       [738733, 738763],
    -       [738764, 738793],
    -       [738794, 738824],
    -       [738825, 738854],
    -       [738855, 738885]])
    -
    -
    -
    -
    -

    Maybe instead we have the start and end time in a pandas dataframe. Here is an example of how to create the 2D array from your pandas dataframe. Here we are creating a dummy dataframe first for you.

    -
    -
    -
    # Function to create a Pandas DataFrame of start and end days for each month in a year
    -import pandas as pd
    -def create_dataframe(year):
    -    months = []
    -    for month in range(1, 13):
    -        first_day_of_month = datetime.date(year, month, 1)
    -        last_day_of_month = datetime.date(year, month % 12 + 1, 1) if month != 12 else datetime.date(year + 1, 1, 1)
    -        end_of_month = (last_day_of_month - datetime.timedelta(days=1)).toordinal()
    -        months.append({'Month': month, 'Start Day': first_day_of_month.toordinal(), 'End Day': end_of_month})
    -    return pd.DataFrame(months)
    -
    -# Create a Pandas DataFrame for the start and end days of each month in 2023
    -df_2023 = create_dataframe(2023)
    -df_2023
    +
    [19388, 19416, 19447, 19477, 19508, 19538, 19569, 19600, 19630, 19661, 19691, 19722]
     
    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    MonthStart DayEnd Day
    01738521738551
    12738552738579
    23738580738610
    34738611738640
    45738641738671
    56738672738701
    67738702738732
    78738733738763
    89738764738793
    910738794738824
    1011738825738854
    1112738855738885
    -
    +

    Let’s now combine these into a 2D array for the bounds of each month

    -
    time_bounds_2d_array = df_2023[['Start Day', 'End Day']].to_numpy()
    -time_bounds_2d_array
    +
    time_bounds_2d_array = np.array(list(zip(days_since_epoch_month_start, days_since_epoch_month_end)))
    +print(time_bounds_2d_array)
     
    -
    array([[738521, 738551],
    -       [738552, 738579],
    -       [738580, 738610],
    -       [738611, 738640],
    -       [738641, 738671],
    -       [738672, 738701],
    -       [738702, 738732],
    -       [738733, 738763],
    -       [738764, 738793],
    -       [738794, 738824],
    -       [738825, 738854],
    -       [738855, 738885]])
    +
    [[19358 19388]
    + [19389 19416]
    + [19417 19447]
    + [19448 19477]
    + [19478 19508]
    + [19509 19538]
    + [19539 19569]
    + [19570 19600]
    + [19601 19630]
    + [19631 19661]
    + [19662 19691]
    + [19692 19722]]
     
    -

    Now we need to create a new variable in our xarray object for the time bounds. This variable needs to have 2 dimensions. time of course, but also another dimension that we will call nv for number of vertices. This dimension will have a value of 2 in this case because the cell provides information on only the minimum and maximum time.

    +

    Now we need to create a new variable in our xarray object for the time bounds. This variable needs to have 2 dimensions. time of course, but also another dimension that we will call nv for number of vertices. This dimension will have a value of 2 in this case because the cell provides information on only the minimum and maximum time.

    xrds = xrds.expand_dims(nv=2) # Creating new dimension
    @@ -1390,26 +1256,26 @@ 

    Maximum monthly temperaturestime_bounds variable defines the bounds of the time variable. We are using cell_methods to state that values are the maximums within each cell with respect to time.

    +

    Now we need to add metadata to make this machine readable. Below we are saying that the time_bounds variable defines the bounds of the time variable. We are using cell_methods to state that values are the maximums within each cell with respect to time.

    xrds['time'].attrs['bounds'] = 'time_bounds'
    @@ -1902,92 +1768,11 @@ 

    Ice core data -

    Cell methods when you have multiple bounds#

    -

    Suppose you have some wind speed model data for a grid of latitude and longitude points. Your model gives you one value for each 0.1 degree cell. You export some variables from your model every second, but your only want to publish the mean values per hour.

    -

    You want to specify the beginning and end time of your cell in your NetCDF file, and state that values are the mean values per hour.

    -

    In this case, you will have bounds for your latitude, longitude and time.

    -
    -
    -
    import numpy as np
    -
    -latitudes = np.arange(58, 62.1, 0.1)
    -longitudes = np.arange(9, 13, 0.1)
    -timestamps_hours = [
    -    datetime.datetime(2023, 6, 11, 0, 0, 0),
    -    datetime.datetime(2023, 6, 12, 0, 0, 0),
    -    datetime.datetime(2023, 6, 13, 0, 0, 0),
    -    datetime.datetime(2023, 6, 14, 0, 0, 0),
    -    datetime.datetime(2023, 6, 15, 0, 0, 0),
    -    datetime.datetime(2023, 6, 16, 0, 0, 0)
    -]
    -
    -num_latitudes = len(latitudes)
    -num_longitudes = len(longitudes)
    -num_timestamps = len(timestamps_hours)
    -
    -# Generating random wind speeds (assuming a range of 0 to 5 m/s for demonstration purposes)
    -mean_wind_speeds = np.random.rand(num_latitudes, num_longitudes, num_timestamps) * 5
    -
    -xrds = xr.Dataset(
    -    coords={
    -        'time': times,
    -        'latitude': latitudes,
    -        'longitude', longitudes
    -    },
    -    data_vars={
    -        'wind_speed': (['latitude','longitude','time'], wind_speed_3d_array)
    -    } 
    -)
    -
    -xrds = xrds.expand_dims(nv=2)
    -
    -
    -
    -
    -
      Cell In[10], line 25
    -    'longitude', longitudes
    -              ^
    -SyntaxError: ':' expected after dictionary key
    -
    -
    -
    -
    -
    -
    -
    times = [0,1,2,3,4]
    -latitudes = [30.1
    -
    -xrds = xr.Dataset(
    -    coords={
    -        'time': times,
    -        'latitude': latitudes,
    -        'longitude', longitudes
    -    },
    -    data_vars={
    -        'wind_speed': (['latitude','longitude','time'], wind_speed_3d_array)
    -    } 
    -)
    -
    -xrds = xrds.expand_dims(nv=2)
    -
    -
    -
    -
    -
      Cell In[1], line 8
    -    'longitude', longitudes
    -              ^
    -SyntaxError: ':' expected after dictionary key
    -
    -
    -
    + [30, 38]])

    • depth
      PandasIndex
      PandasIndex(Index([0, 10, 20, 30], dtype='int64', name='depth'))
  • @@ -2022,6 +1807,15 @@

    Cell methods when you have multiple bounds @@ -2039,7 +1833,6 @@

    Cell methods when you have multiple bounds
  • Maximum monthly temperatures
  • Ice core data
  • -
  • Cell methods when you have multiple bounds
  • diff --git a/_sources/03_extracting_data_to_different_formats.ipynb b/_sources/03_extracting_data_to_different_formats.ipynb index eaac7b2..9dd3bc7 100644 --- a/_sources/03_extracting_data_to_different_formats.ipynb +++ b/_sources/03_extracting_data_to_different_formats.ipynb @@ -4394,7 +4394,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/_sources/04_creating_a_cfnetcdf_file.ipynb b/_sources/04_creating_a_cfnetcdf_file.ipynb index c20186a..70bff87 100644 --- a/_sources/04_creating_a_cfnetcdf_file.ipynb +++ b/_sources/04_creating_a_cfnetcdf_file.ipynb @@ -5281,7 +5281,7 @@ "And remember we might want to select additional applicable global attributes from this section of the CF conventions:\n", "https://cfconventions.org/Data/cf-conventions/cf-conventions-1.11/cf-conventions.html#attribute-appendix\n", "\n", - "Go through and add each required attribute and any others you wish to. You are also welcome to add any global attributes from the CF conventions as well as any custom attributes on top of these requirements. Similarly to variable attributes, this can either be done one by one or all in one in a dictionary. " + "Go through and add each required attribute and any others you wish to. You are also welcome to add any custom attributes on top of these requirements. Similarly to variable attributes, this can either be done one by one or all in one in a dictionary. " ] }, { @@ -5323,7 +5323,7 @@ "id": "3d9fd5f0", "metadata": {}, "source": [ - "In this case, it makes sense to some attributes based on information we have already provided." + "In this case, it makes sense to add some attributes based on information we have already provided." ] }, { @@ -5486,7 +5486,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/_sources/08_ancillary_variables.ipynb b/_sources/08_ancillary_variables.ipynb index 9a58640..2c68267 100644 --- a/_sources/08_ancillary_variables.ipynb +++ b/_sources/08_ancillary_variables.ipynb @@ -458,12 +458,12 @@ "\n", "We need to create a new variable for the flags.\n", "\n", - "Flags are stored as numbers, and with the meanings for the numbers stored as a variable attribute. The `flag_meanings` should be separated by spaces - so don't include spaces in any of the terms you use! The length of the `flag values` and `flag_meanings` should be the same. " + "Flags are stored as numbers, and with the meanings for the numbers stored as a variable attribute. The *flag_meanings* should be separated by spaces - so don't include spaces in any of the terms you use! The length of the *flag values* and *flag_meanings* should be the same. " ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "id": "a942a457", "metadata": {}, "outputs": [], @@ -477,7 +477,7 @@ "id": "96a656ec", "metadata": {}, "source": [ - "So for example, a value of 2 means 'probably_good_data'\n", + "So for example, a value of 2 means *probably_good_data*.\n", "\n", "You might wonder which conventions these quality flag values and meanings adhere to. In this case, we are following the OceanSITES Manual v 1.4.\n", "http://www.oceansites.org/docs/oceansites_data_format_reference_manual.pdf\n", @@ -895,7 +895,7 @@ "id": "ad00e25c", "metadata": {}, "source": [ - "Now we need to state that the new 'Chlorophyll_A_quality_flags' variable is related to the 'Chlorophyll A' variable." + "Now we need to state that the new *Chlorophyll_A_quality_flags* variable is related to the *Chlorophyll A* variable." ] }, { @@ -913,10 +913,10 @@ "id": "3ba86208", "metadata": {}, "source": [ - "Finally we need to add our metadata to the ancillary variable to describe it. There are a lot of standard names for different types of flags. Search for 'flag' here to find a suitable `standard_name` for you. \n", + "Finally we need to add our metadata to the ancillary variable to describe it. There are a lot of standard names for different types of flags. Search for *flag* here to find a suitable *standard_name* for you. \n", "https://cfconventions.org/Data/cf-standard-names/current/build/cf-standard-name-table.html\n", "\n", - "The CF conventions also has standardised variable attributes you can use for `flag_values`and `flag_meanings`. You often see the `valid_range` attribute used here too to explicitely show that any values outside of that range are invalid. You could use `valid_min` and `valid_max` used instead." + "The CF conventions also has standardised variable attributes you can use for *flag_values* and *flag_meanings*. You often see the *valid_range* attribute used here too to explicitely show that any values outside of that range are invalid. You could use *valid_min* and *valid_max* used instead." ] }, { @@ -1342,7 +1342,7 @@ "id": "667e8976", "metadata": {}, "source": [ - "Make sure you refer to the conventions you are following for your quality flags in your `Conventions` global attribute, for example" + "Make sure you refer to the conventions you are following for your quality flags in your *Conventions* global attribute, for example" ] }, { @@ -1755,7 +1755,7 @@ "metadata": {}, "source": [ "## Retrieving only good_quality data\n", - "Suppose we want to retrieve only the good_quality `Chlorophyll_A` data, where `Chlorophyll_A_quality_flags = 1`" + "Suppose we want to retrieve only the good_quality *Chlorophyll_A* data, where *Chlorophyll_A_quality_flags = 1*" ] }, { @@ -3011,7 +3011,7 @@ "id": "8e994299", "metadata": {}, "source": [ - "More work needs to be done to expand the CF conventions to standardise ancillary data. At the time of writing, a `standard_name` for the volume of sea water filtered does not exist. \n", + "More work needs to be done to expand the CF conventions to standardise ancillary data. At the time of writing, a *standard_name* for the volume of sea water filtered does not exist. \n", "\n", "This is where the scientific community can help! \n", "\n", diff --git a/_sources/09_cells_and_cell_methods.ipynb b/_sources/09_cells_and_cell_methods.ipynb index 6760eea..2b387c5 100644 --- a/_sources/09_cells_and_cell_methods.ipynb +++ b/_sources/09_cells_and_cell_methods.ipynb @@ -19,12 +19,12 @@ "\n", "Let's start with the basic code to create our xarray object for a time series of temperature values.\n", "\n", - "Months and years not recommended in CF because of potential for confusion with leap years. Therefore, we can use days since 1970-01-01 (the epoch) for first day of each month for 2023." + "Units of *months since date* or *years since date* are not recommended in CF because of potential for confusion with leap years. Therefore, we can use days since 1970-01-01 (the epoch) for first day of each month for 2023." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 23, "id": "8971836c", "metadata": {}, "outputs": [ @@ -38,23 +38,25 @@ ], "source": [ "import datetime\n", + "import xarray as xr\n", + "import calendar\n", + "import numpy as np\n", + "\n", + "days_since_epoch_month_start = []\n", + "year = 2023\n", + "epoch = datetime.date(1970, 1, 1)\n", "\n", - "# Function to calculate days since 1970-01-01 for the start of each month in a year\n", - "def get_days_since_epoch(year):\n", - " days_since_epoch = []\n", - " for month in range(1, 13):\n", - " first_day_of_month = datetime.date(year, month, 1)\n", - " days_since_epoch.append((first_day_of_month - datetime.date(1970, 1, 1)).days)\n", - " return days_since_epoch\n", + "for month in range(1, 13):\n", + " first_day_of_month = datetime.date(year, month, 1)\n", + " time_difference = (first_day_of_month - epoch).days\n", + " days_since_epoch_month_start.append(time_difference)\n", "\n", - "# Get days since 1970-01-01 for the start of each month in 2023\n", - "days_since_epoch_2023 = get_days_since_epoch(2023)\n", - "print(days_since_epoch_2023)\n" + "print(days_since_epoch_month_start)\n" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 15, "id": "5a699b32", "metadata": {}, "outputs": [ @@ -429,11 +431,11 @@ "Coordinates:\n", " * time (time) int64 19358 19389 19417 19448 ... 19631 19662 19692\n", "Data variables:\n", - " Temperature (time) float64 4.6 5.2 7.1 12.3 17.8 ... 22.8 19.0 14.2 8.8 6.1

    " + " Temperature (time) float64 4.6 5.2 7.1 12.3 17.8 ... 22.8 19.0 14.2 8.8 6.1
    " ], "text/plain": [ "\n", @@ -444,20 +446,17 @@ " Temperature (time) float64 4.6 5.2 7.1 12.3 17.8 ... 22.8 19.0 14.2 8.8 6.1" ] }, - "execution_count": 2, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import xarray as xr\n", - "\n", - "days_since_epoch = [19358, 19389, 19417, 19448, 19478, 19509, 19539, 19570, 19601, 19631, 19662, 19692]\n", "maximum_monthly_temperatures = [4.6,5.2,7.1,12.3,17.8,21.3,24.6,22.8,19.0,14.2,8.8,6.1]\n", "\n", "xrds = xr.Dataset(\n", " coords={\n", - " 'time': days_since_epoch\n", + " 'time': days_since_epoch_month_start\n", " },\n", " data_vars={\n", " 'Temperature': ('time', maximum_monthly_temperatures)\n", @@ -484,242 +483,76 @@ "id": "b3b2a1c6", "metadata": {}, "source": [ - "But our values are not representative of just one day; they are maximum values for the month! So we need to include some bounds for the month. First, we need to create a 2D array of values." + "But our values are not representative of just one day; they are maximum values for the month! How can we let the user know is in an unambiguous, machine-readable way? Remember - the long name is free text, so we can't rely on this.\n", + "\n", + "We need to include some bounds to define where each month begins and end. Let's now calculate the number of days since the epoch for the last day of each month." ] }, { "cell_type": "code", - "execution_count": 3, - "id": "a831d1b4", + "execution_count": 17, + "id": "3e6d0da1-fd27-4328-b734-52a0c130bd74", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([[738521, 738551],\n", - " [738552, 738579],\n", - " [738580, 738610],\n", - " [738611, 738640],\n", - " [738641, 738671],\n", - " [738672, 738701],\n", - " [738702, 738732],\n", - " [738733, 738763],\n", - " [738764, 738793],\n", - " [738794, 738824],\n", - " [738825, 738854],\n", - " [738855, 738885]])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "[19388, 19416, 19447, 19477, 19508, 19538, 19569, 19600, 19630, 19661, 19691, 19722]\n" + ] } ], "source": [ - "import numpy as np\n", - "time_bounds_2d_array = np.array([\n", - " [738521, 738551],\n", - " [738552, 738579],\n", - " [738580, 738610],\n", - " [738611, 738640],\n", - " [738641, 738671],\n", - " [738672, 738701],\n", - " [738702, 738732],\n", - " [738733, 738763],\n", - " [738764, 738793],\n", - " [738794, 738824],\n", - " [738825, 738854],\n", - " [738855, 738885]\n", - "])\n", - "time_bounds_2d_array" + "days_since_epoch_month_end = []\n", + "\n", + "for month in range(1, 13):\n", + " # Find the last day of the month\n", + " last_day_of_month = calendar.monthrange(year, month)[1]\n", + " last_day_date = datetime.date(year, month, last_day_of_month)\n", + " \n", + " # Calculate the days since epoch for the last day of the month\n", + " time_difference = (last_day_date - epoch).days\n", + " days_since_epoch_month_end.append(time_difference)\n", + "\n", + "print(days_since_epoch_month_end)" ] }, { "cell_type": "markdown", - "id": "8d064d17", + "id": "0e8d1044-53b1-4a01-9b90-ad5b0d1f6175", "metadata": {}, "source": [ - "Maybe instead we have the start and end time in a pandas dataframe. Here is an example of how to create the 2D array from your pandas dataframe. Here we are creating a dummy dataframe first for you." + "Let's now combine these into a 2D array for the bounds of each month" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "46d4a8f6", + "execution_count": 25, + "id": "222b9828-01d8-40d0-a1bc-0e9be5f92916", "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    MonthStart DayEnd Day
    01738521738551
    12738552738579
    23738580738610
    34738611738640
    45738641738671
    56738672738701
    67738702738732
    78738733738763
    89738764738793
    910738794738824
    1011738825738854
    1112738855738885
    \n", - "
    " - ], - "text/plain": [ - " Month Start Day End Day\n", - "0 1 738521 738551\n", - "1 2 738552 738579\n", - "2 3 738580 738610\n", - "3 4 738611 738640\n", - "4 5 738641 738671\n", - "5 6 738672 738701\n", - "6 7 738702 738732\n", - "7 8 738733 738763\n", - "8 9 738764 738793\n", - "9 10 738794 738824\n", - "10 11 738825 738854\n", - "11 12 738855 738885" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Function to create a Pandas DataFrame of start and end days for each month in a year\n", - "import pandas as pd\n", - "def create_dataframe(year):\n", - " months = []\n", - " for month in range(1, 13):\n", - " first_day_of_month = datetime.date(year, month, 1)\n", - " last_day_of_month = datetime.date(year, month % 12 + 1, 1) if month != 12 else datetime.date(year + 1, 1, 1)\n", - " end_of_month = (last_day_of_month - datetime.timedelta(days=1)).toordinal()\n", - " months.append({'Month': month, 'Start Day': first_day_of_month.toordinal(), 'End Day': end_of_month})\n", - " return pd.DataFrame(months)\n", - "\n", - "# Create a Pandas DataFrame for the start and end days of each month in 2023\n", - "df_2023 = create_dataframe(2023)\n", - "df_2023" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7112b982", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[738521, 738551],\n", - " [738552, 738579],\n", - " [738580, 738610],\n", - " [738611, 738640],\n", - " [738641, 738671],\n", - " [738672, 738701],\n", - " [738702, 738732],\n", - " [738733, 738763],\n", - " [738764, 738793],\n", - " [738794, 738824],\n", - " [738825, 738854],\n", - " [738855, 738885]])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[19358 19388]\n", + " [19389 19416]\n", + " [19417 19447]\n", + " [19448 19477]\n", + " [19478 19508]\n", + " [19509 19538]\n", + " [19539 19569]\n", + " [19570 19600]\n", + " [19601 19630]\n", + " [19631 19661]\n", + " [19662 19691]\n", + " [19692 19722]]\n" + ] } ], "source": [ - "time_bounds_2d_array = df_2023[['Start Day', 'End Day']].to_numpy()\n", - "time_bounds_2d_array" + "time_bounds_2d_array = np.array(list(zip(days_since_epoch_month_start, days_since_epoch_month_end)))\n", + "print(time_bounds_2d_array)" ] }, { @@ -727,12 +560,12 @@ "id": "25476a4e", "metadata": {}, "source": [ - "Now we need to create a new variable in our xarray object for the time bounds. This variable needs to have 2 dimensions. `time` of course, but also another dimension that we will call `nv` for number of vertices. This dimension will have a value of 2 in this case because the cell provides information on only the minimum and maximum time." + "Now we need to create a new variable in our xarray object for the time bounds. This variable needs to have 2 dimensions. *time* of course, but also another dimension that we will call *nv* for number of vertices. This dimension will have a value of 2 in this case because the cell provides information on only the minimum and maximum time." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 26, "id": "e9d8bcf0", "metadata": {}, "outputs": [ @@ -1109,24 +942,24 @@ "Dimensions without coordinates: nv\n", "Data variables:\n", " Temperature (nv, time) float64 4.6 5.2 7.1 12.3 17.8 ... 19.0 14.2 8.8 6.1\n", - " time_bounds (time, nv) int64 738521 738551 738552 ... 738854 738855 738885

  • time_bounds
    (time, nv)
    int64
    19358 19388 19389 ... 19692 19722
    array([[19358, 19388],\n",
    +       "       [19389, 19416],\n",
    +       "       [19417, 19447],\n",
    +       "       [19448, 19477],\n",
    +       "       [19478, 19508],\n",
    +       "       [19509, 19538],\n",
    +       "       [19539, 19569],\n",
    +       "       [19570, 19600],\n",
    +       "       [19601, 19630],\n",
    +       "       [19631, 19661],\n",
    +       "       [19662, 19691],\n",
    +       "       [19692, 19722]])
    • time
      PandasIndex
      PandasIndex(Index([19358, 19389, 19417, 19448, 19478, 19509, 19539, 19570, 19601, 19631,\n",
      +       "       19662, 19692],\n",
      +       "      dtype='int64', name='time'))
  • " ], "text/plain": [ "\n", @@ -1136,10 +969,10 @@ "Dimensions without coordinates: nv\n", "Data variables:\n", " Temperature (nv, time) float64 4.6 5.2 7.1 12.3 17.8 ... 19.0 14.2 8.8 6.1\n", - " time_bounds (time, nv) int64 738521 738551 738552 ... 738854 738855 738885" + " time_bounds (time, nv) int64 19358 19388 19389 19416 ... 19691 19692 19722" ] }, - "execution_count": 6, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1156,12 +989,12 @@ "id": "15ef44f3", "metadata": {}, "source": [ - "Now we need to add metadata to make this machine readable. Below we are saying that the `time_bounds` variable defines the bounds of the `time` variable. We are using `cell_methods` to state that values are the maximums within each cell with respect to time." + "Now we need to add metadata to make this machine readable. Below we are saying that the *time_bounds* variable defines the bounds of the *time* variable. We are using *cell_methods* to state that values are the maximums within each cell with respect to time." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 27, "id": "b4d706d0", "metadata": {}, "outputs": [], @@ -1724,94 +1557,6 @@ "xrds\n", "\n" ] - }, - { - "cell_type": "markdown", - "id": "a82b4169", - "metadata": {}, - "source": [ - "## Cell methods when you have multiple bounds\n", - "\n", - "Suppose you have some wind speed model data for a grid of latitude and longitude points. Your model gives you one value for each 0.1 degree cell. You export some variables from your model every second, but your only want to publish the mean values per hour.\n", - "\n", - "You want to specify the beginning and end time of your cell in your NetCDF file, and state that values are the mean values per hour.\n", - "\n", - "In this case, you will have bounds for your latitude, longitude and time." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c967446a", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "latitudes = np.arange(58, 62.1, 0.1)\n", - "longitudes = np.arange(9, 13, 0.1)\n", - "timestamps_hours = [\n", - " datetime.datetime(2023, 6, 11, 0, 0, 0),\n", - " datetime.datetime(2023, 6, 12, 0, 0, 0),\n", - " datetime.datetime(2023, 6, 13, 0, 0, 0),\n", - " datetime.datetime(2023, 6, 14, 0, 0, 0),\n", - " datetime.datetime(2023, 6, 15, 0, 0, 0),\n", - " datetime.datetime(2023, 6, 16, 0, 0, 0)\n", - "]\n", - "\n", - "num_latitudes = len(latitudes)\n", - "num_longitudes = len(longitudes)\n", - "num_timestamps = len(timestamps_hours)\n", - "\n", - "# Generating random wind speeds (assuming a range of 0 to 5 m/s for demonstration purposes)\n", - "mean_wind_speeds = np.random.rand(num_latitudes, num_longitudes, num_timestamps) * 5\n", - "\n", - "xrds = xr.Dataset(\n", - " coords={\n", - " 'time': times,\n", - " 'latitude': latitudes,\n", - " 'longitude', longitudes\n", - " },\n", - " data_vars={\n", - " 'wind_speed': (['latitude','longitude','time'], wind_speed_3d_array)\n", - " } \n", - ")\n", - "\n", - "xrds = xrds.expand_dims(nv=2)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d09c9446", - "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "':' expected after dictionary key (3422837419.py, line 8)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m Cell \u001b[0;32mIn[1], line 8\u001b[0;36m\u001b[0m\n\u001b[0;31m 'longitude', longitudes\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m ':' expected after dictionary key\n" - ] - } - ], - "source": [ - "times = [0,1,2,3,4]\n", - "latitudes = [30.1\n", - "\n", - "xrds = xr.Dataset(\n", - " coords={\n", - " 'time': times,\n", - " 'latitude': latitudes,\n", - " 'longitude', longitudes\n", - " },\n", - " data_vars={\n", - " 'wind_speed': (['latitude','longitude','time'], wind_speed_3d_array)\n", - " } \n", - ")\n", - "\n", - "xrds = xrds.expand_dims(nv=2)" - ] } ], "metadata": { @@ -1830,7 +1575,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/genindex.html b/genindex.html index 3646df9..274ec35 100644 --- a/genindex.html +++ b/genindex.html @@ -173,6 +173,7 @@
  • 06: Creating multiple CF-NetCDF files in one go
  • 07: Combining data from multiple netcdf files
  • 08: Ancillary variables
  • +
  • 09: Cells and cell methods
  • diff --git a/intro.html b/intro.html index c9accda..abf8b21 100644 --- a/intro.html +++ b/intro.html @@ -175,6 +175,7 @@
  • 06: Creating multiple CF-NetCDF files in one go
  • 07: Combining data from multiple netcdf files
  • 08: Ancillary variables
  • +
  • 09: Cells and cell methods
  • @@ -386,6 +387,7 @@

    NetCDF in Python: From beginner to pro06: Creating multiple CF-NetCDF files in one go
  • 07: Combining data from multiple netcdf files
  • 08: Ancillary variables
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