From 9db752fb407fe49815825355dabac2339787468e Mon Sep 17 00:00:00 2001 From: MarkDownBot Date: Mon, 11 Dec 2023 13:24:39 +0000 Subject: [PATCH 01/10] Updated Altimetry notebook pages from coast repo. --- .../tidegauge_validation_tutorial.md | 158 +++++++++--------- .../Notebooks/Tidegauge/tidetable_tutorial.md | 4 +- .../tidegauge_validation_tutorial_22_0.png | Bin 51113 -> 50376 bytes .../tidegauge_validation_tutorial_37_0.png | Bin 63286 -> 65517 bytes .../tidetable_tutorial_14_1.png | Bin 17176 -> 16231 bytes 5 files changed, 81 insertions(+), 81 deletions(-) diff --git a/content/en/docs/Examples/Notebooks/Tidegauge/tidegauge_validation_tutorial.md b/content/en/docs/Examples/Notebooks/Tidegauge/tidegauge_validation_tutorial.md index 4f921fa78a..9a562074ed 100644 --- a/content/en/docs/Examples/Notebooks/Tidegauge/tidegauge_validation_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Tidegauge/tidegauge_validation_tutorial.md @@ -84,7 +84,7 @@ We can create our empty tidegauge object: tidegauge = coast.Tidegauge() ``` - Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x5627e1b35980 initialised The `Tidegauge` class contains multiple methods for reading different typical @@ -481,7 +481,7 @@ Coordinates: Dimensions without coordinates: id_dim, t_dim Data variables: ssh (id_dim, t_dim) float64 2.818 2.823 2.871 ... 3.214 3.257 3.371 - qc_flags (id_dim, t_dim) int64 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1 1
    • @@ -951,7 +951,7 @@ Attributes: end_date: 31oct2020-23.45.00 contributor: national_oceanography_centre,_liverpool datum_information: the_data_refer_to_admiralty_chart_datum_(acd) - parameter_code: aslvbg02_=_surface_elevation_(unspecified_datum)_of_t...
      • port :
        p234
        site :
        liverpool,_gladstone_dock
        start_date :
        01oct2020-00.00.00
        end_date :
        31oct2020-23.45.00
        contributor :
        national_oceanography_centre,_liverpool
        datum_information :
        the_data_refer_to_admiralty_chart_datum_(acd)
        parameter_code :
        aslvbg02_=_surface_elevation_(unspecified_datum)_of_the_water_body_by_bubbler_tide_gauge_(second_sensor)
      • @@ -1089,7 +1089,7 @@ tt = xr.open_dataset(fn_tg) obs = coast.Tidegauge(dataset=tt) ``` - Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x5627e1b35980 initialised @@ -1100,7 +1100,7 @@ obs = coast.Tidegauge(dataset=tt) obs.dataset = obs.dataset.set_coords("time") ``` - Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x5627e1b35980 initialised ## Quick plotting to visualise Tidegauge data @@ -1168,7 +1168,7 @@ obs.isel(id_dim=stn_id).plot_timeseries(date_start=np.datetime64('2007-01-01'), (
        , - ) + ) @@ -1200,9 +1200,9 @@ obs_lower = obs_cut.subset_indices_lonlat_box(lonbounds = [-9, -3], fig, ax = obs_upper.plot_on_map_multiple([obs_upper, obs_lower], color_var_str="latitude") ``` - Tidegauge object at 0x562173632980 initialised - Tidegauge object at 0x562173632980 initialised - Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x5627e1b35980 initialised @@ -1225,7 +1225,7 @@ nemo.dataset = nemo.dataset.rename({"depth_0": "depth"}) #nemo.dataset = nemo.dataset[["ssh", "landmask"]] ``` - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:278: UserWarning: The specified chunks separate the stored chunks along dimension "time_counter" starting at index 2. This could degrade performance. Instead, consider rechunking after loading. + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:282: UserWarning: The specified chunks separate the stored chunks along dimension "time_counter" starting at index 2. This could degrade performance. Instead, consider rechunking after loading. interpolate model onto obs locations @@ -1256,7 +1256,7 @@ tidegauge_from_model = obs.obs_operator(nemo, time_interp='nearest') Indexing model data at tide gauge locations.. Calculating interpolation distances. Interpolating in time... - Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x5627e1b35980 initialised However, the new `tidegauge_from_model` will the same number of time entries as the `obs` data, rather than the model data (so this will include lots of empty values). So, for a more useful demonstration we trim the observed gauge data so it better matches the model data. @@ -1269,7 +1269,7 @@ end_date = datetime.datetime(2007, 1, 31) obs = obs.time_slice(start_date, end_date) ``` - Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x5627e1b35980 initialised ### Interpolate model data onto obs locations @@ -1287,7 +1287,7 @@ model_timeseries.dataset Indexing model data at tide gauge locations.. Calculating interpolation distances. Interpolating in time... - Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x5627e1b35980 initialised @@ -1683,7 +1683,7 @@ Attributes: timeStamp: 2019-Dec-26 04:35:28 GMT uuid: 96cae459-d3a1-4f4f-b82b-9259179f95f7 history: Tue May 19 12:07:51 2020: ncks -v votemper,sossheig -d time... - NCO: 4.4.7
      • name :
        AMM7_1d_20070101_20070131_25hourm_grid_T
        description :
        ocean T grid variables, 25h meaned
        title :
        ocean T grid variables, 25h meaned
        Conventions :
        CF-1.6
        timeStamp :
        2019-Dec-26 04:35:28 GMT
        uuid :
        96cae459-d3a1-4f4f-b82b-9259179f95f7
        history :
        Tue May 19 12:07:51 2020: ncks -v votemper,sossheig -d time_counter,0,30,5 AMM7_1d_20070101_20070131_25hourm_grid_T.nc example_data.nc
        NCO :
        4.4.7
      • @@ -1932,8 +1932,8 @@ tganalysis = coast.TidegaugeAnalysis() obs_new, model_new = tganalysis.match_missing_values(obs.dataset.ssh, model_timeseries.dataset.ssh) ``` - Tidegauge object at 0x562173632980 initialised - Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x5627e1b35980 initialised Although we input data arrays to the above routine, it returns two new Tidegauge objects. Now you have equivalent and comparable sets of time series that can be easily compared. @@ -1959,8 +1959,8 @@ model_new = tganalysis.demean_timeseries(model_new.dataset) # easily compared. ``` - Tidegauge object at 0x562173632980 initialised - Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x5627e1b35980 initialised @@ -2166,7 +2166,7 @@ tide_obs = tganalysis.reconstruct_tide_utide(obs_new.dataset.time, ha_obs) prep/calcs ... done. prep/calcs ... done. prep/calcs ... done. - Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x5627e1b35980 initialised prep/calcs ... done. prep/calcs ... done. prep/calcs ... done. @@ -2216,7 +2216,7 @@ tide_obs = tganalysis.reconstruct_tide_utide(obs_new.dataset.time, ha_obs) prep/calcs ... done. prep/calcs ... done. prep/calcs ... done. - Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x5627e1b35980 initialised Get new TidegaugeMultiple objects containing non tidal residuals: @@ -2230,8 +2230,8 @@ ntr_obs = tganalysis.calculate_non_tidal_residuals(obs_new.dataset.ssh, tide_obs ntr_obs.dataset ``` - Tidegauge object at 0x562173632980 initialised - Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x5627e1b35980 initialised @@ -2609,7 +2609,7 @@ Coordinates: site_name (id_dim) <U25 'Harwich' 'Mumbles' 'Ullapool' ... 'N/A' 'N/A' Dimensions without coordinates: t_dim, id_dim Data variables: - ntr (id_dim, t_dim) float64 0.4182 0.4182 0.4182 ... -0.02699 0.2945
        • @@ -2720,7 +2720,7 @@ dx0.dataset ``` - Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x5627e1b35980 initialised @@ -3098,7 +3098,7 @@ Coordinates: Dimensions without coordinates: t_dim, id_dim Data variables: time (t_dim) datetime64[ns] 2007-01-01 ... 2007-01-30T23:00:00 - ssh (id_dim, t_dim) float64 nan nan nan nan nan ... nan nan nan nan
          • @@ -3635,7 +3635,7 @@ Data variables: peak_count_ntr (id_dim, threshold) float64 40.0 40.0 ... 2.0 1.0 time_over_threshold_ntr (id_dim, threshold) float64 720.0 720.0 ... 5.0 4.0 dailymax_count_ntr (id_dim, threshold) float64 30.0 30.0 ... 2.0 1.0 - monthlymax_count_ntr (id_dim, threshold) float64 1.0 1.0 1.0 ... 1.0 1.0
          • @@ -3805,7 +3805,7 @@ plt.plot(thresh_obs.threshold[22], normalised_event_count[22], 'r+', markersize= - [] + [] @@ -3831,8 +3831,8 @@ ssh_diff = tganalysis.difference(obs_new.dataset, model_new.dataset) ntr_diff.dataset ``` - Tidegauge object at 0x562173632980 initialised - Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x5627e1b35980 initialised @@ -4212,10 +4212,10 @@ Dimensions without coordinates: t_dim, id_dim Data variables: diff_ntr (id_dim, t_dim) float64 nan nan nan ... 0.05885 0.3435 abs_diff_ntr (id_dim, t_dim) float64 nan nan nan ... 0.05885 0.3435 - square_diff_ntr (id_dim, t_dim) float64 nan nan nan ... 0.003464 0.118
          • diff --git a/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md b/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md index 64507a1787..87dc52dd38 100644 --- a/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md @@ -72,7 +72,7 @@ Initiate a TideGauge object, if a filename is passed it assumes it is a GESLA ty tg = coast.Tidegauge() ``` - Tidegauge object at 0x56422f0cb980 initialised + Tidegauge object at 0x555cb5b8d980 initialised Specify the data read as a High Low Water dataset. @@ -103,7 +103,7 @@ tg.dataset.plot.scatter(x="time", y="ssh") - + diff --git a/static/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_22_0.png b/static/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_22_0.png index a8f6f84d356c43fd8c1960f18fa11207bd3122bf..750d651e1330673aac069303060f8ae22258da3d 100644 GIT binary patch literal 50376 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zmPy2KYE(|7{rOj|OONytHT(!i-?_?!aCxoe`kz-^?8 z1D_+=f`WoVmMx-~FvzK|t@5nFHmT;0=8B zDiKmiKSsUEkhjRuV=|^Cr^2Cpl2di9cGWAmg&aAP`2lF~-g&~1l;v2QGa`Nz@K$+p zE(CTg%{T6=GY`XsB&&V?{6l~!Rs0VCsEKdDayzy7APWLWs^X9{2qXdSot8Xs^TRM8Gz1eob^ewZa*nG_KA_Bp8W>>-O<$_$hr*B z^Z6344dkJYYfZBNM)LeDntWWAbbGN0(z0&b5g`IOmW7 zu_T@aGSI})l6hW7)1Rw;TEYl_V#|&leInn5|3HIGz|aHaBnA($D$kNqQTY8V<<=Wi z$Q6+l63hUOS>`8j^@Ib%6H|wf)9B?qlw$-;VE)W6`U@|QsX`FcrNsXBb1k{}5$qT~ zFAm91ToF^ltt?LlbZ`U(XFKAFI*OD9 ce0f!nPC>-xn={5p2a3!Y`O}FfFW>q<04GPW*Z=?k From 50c1daf8ad71902ca0790582da4eb01e85f38384 Mon Sep 17 00:00:00 2001 From: MarkDownBot Date: Mon, 11 Dec 2023 13:25:13 +0000 Subject: [PATCH 02/10] Updated Gridded notebook pages from coast repo. --- .../Gridded/introduction_to_gridded_class.md | 6 +++--- .../Notebooks/Gridded/potential_energy_tutorial.md | 13 ++++++++----- .../Notebooks/Gridded/pycnocline_tutorial.md | 3 ++- .../Gridded/seasia_dic_example_plot_tutorial.md | 2 +- 4 files changed, 14 insertions(+), 10 deletions(-) diff --git a/content/en/docs/Examples/Notebooks/Gridded/introduction_to_gridded_class.md b/content/en/docs/Examples/Notebooks/Gridded/introduction_to_gridded_class.md index a6e8f00cec..0daa3607af 100644 --- a/content/en/docs/Examples/Notebooks/Gridded/introduction_to_gridded_class.md +++ b/content/en/docs/Examples/Notebooks/Gridded/introduction_to_gridded_class.md @@ -63,7 +63,7 @@ We can create a new Gridded object by simple calling `coast.Gridded()`. By passi nemo_t = coast.Gridded(fn_data = fn_nemo_dat, fn_domain = fn_nemo_dom, config=fn_config_t_grid) ``` - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:278: UserWarning: The specified chunks separate the stored chunks along dimension "time_counter" starting at index 2. This could degrade performance. Instead, consider rechunking after loading. + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:282: UserWarning: The specified chunks separate the stored chunks along dimension "time_counter" starting at index 2. This could degrade performance. Instead, consider rechunking after loading. Our new Gridded object `nemo_t` contains a variable called dataset, which holds information on the two files we passed. Let’s have a look at this: @@ -109,7 +109,7 @@ plt.pcolormesh(nemo_t.dataset.longitude, nemo_t.dataset.latitude, nemo_t.dataset - + @@ -183,7 +183,7 @@ plt.ylabel("latitude") plt.show() ``` - /tmp/ipykernel_2672/2498690501.py:2: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh. + /tmp/ipykernel_2729/2498690501.py:2: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh. diff --git a/content/en/docs/Examples/Notebooks/Gridded/potential_energy_tutorial.md b/content/en/docs/Examples/Notebooks/Gridded/potential_energy_tutorial.md index d367f5aff2..262090057f 100644 --- a/content/en/docs/Examples/Notebooks/Gridded/potential_energy_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Gridded/potential_energy_tutorial.md @@ -75,6 +75,9 @@ Zd_mask, kmax, Ikmax = nemo.calculate_vertical_mask(200.) strat = coast.GriddedStratification(nemo) ``` + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/utils.py:494: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`. + + ```python @@ -486,10 +489,10 @@ Coordinates: longitude (y_dim, x_dim) float32 -19.89 -19.78 -19.67 ... 12.78 12.89 13.0 Dimensions without coordinates: t_dim, y_dim, x_dim Data variables: - PEA (t_dim, y_dim, x_dim) float64 nan nan nan nan ... nan nan nan nan

            • diff --git a/content/en/docs/Examples/Notebooks/Gridded/pycnocline_tutorial.md b/content/en/docs/Examples/Notebooks/Gridded/pycnocline_tutorial.md index 874d42cf1b..bee3e552cc 100644 --- a/content/en/docs/Examples/Notebooks/Gridded/pycnocline_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Gridded/pycnocline_tutorial.md @@ -122,7 +122,8 @@ strat = coast.GriddedStratification(nemo_nwes_t) strat.construct_pycnocline_vars( nemo_nwes_t, nemo_nwes_w ) ``` - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/computation.py:821: RuntimeWarning: invalid value encountered in sqrt + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/utils.py:494: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`. + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/computation.py:822: RuntimeWarning: invalid value encountered in sqrt ### Plotting data diff --git a/content/en/docs/Examples/Notebooks/Gridded/seasia_dic_example_plot_tutorial.md b/content/en/docs/Examples/Notebooks/Gridded/seasia_dic_example_plot_tutorial.md index eb72db2dde..1fecd041d5 100644 --- a/content/en/docs/Examples/Notebooks/Gridded/seasia_dic_example_plot_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Gridded/seasia_dic_example_plot_tutorial.md @@ -75,7 +75,7 @@ plt.ylabel("latitude") plt.show() ``` - /tmp/ipykernel_2792/1161426776.py:2: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh. + /tmp/ipykernel_2849/1161426776.py:2: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh. From 5e759208f78ac963ba90a1dcc97ca6a7cb5cc100 Mon Sep 17 00:00:00 2001 From: MarkDownBot Date: Mon, 11 Dec 2023 13:27:08 +0000 Subject: [PATCH 03/10] Updated Profile notebook pages from coast repo. --- .../Profile/introduction_to_profile_class.md | 21 +++++++++++++------ .../Profile/wod_bgc_ragged_example.md | 2 ++ 2 files changed, 17 insertions(+), 6 deletions(-) diff --git a/content/en/docs/Examples/Notebooks/Profile/introduction_to_profile_class.md b/content/en/docs/Examples/Notebooks/Profile/introduction_to_profile_class.md index 9484140a14..d798828756 100644 --- a/content/en/docs/Examples/Notebooks/Profile/introduction_to_profile_class.md +++ b/content/en/docs/Examples/Notebooks/Profile/introduction_to_profile_class.md @@ -138,6 +138,10 @@ processed_profile = profile.process_en4() profile = processed_profile ``` + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/utils.py:494: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`. + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/utils.py:494: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`. + + We can do some simple spatial and temporal manipulations of this data: @@ -197,7 +201,7 @@ fn_nemo_config = path.join(root, "./config/example_nemo_grid_t.json") nemo = coast.Gridded(fn_nemo_dat, fn_nemo_dom, multiple=True, config=fn_nemo_config) ``` - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:278: UserWarning: The specified chunks separate the stored chunks along dimension "time_counter" starting at index 2. This could degrade performance. Instead, consider rechunking after loading. + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:282: UserWarning: The specified chunks separate the stored chunks along dimension "time_counter" starting at index 2. This could degrade performance. Instead, consider rechunking after loading. #### Create a landmask array in Gridded @@ -221,6 +225,8 @@ nemo.dataset = nemo.dataset.rename({"depth_0": "depth"}) # profile methods will model_profiles = profile.obs_operator(nemo) ``` + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/utils.py:494: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`. + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/utils.py:494: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`. /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/coast/data/profile.py:456: UserWarning: Converting non-nanosecond precision timedelta values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time. /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/coast/data/profile.py:456: UserWarning: Converting non-nanosecond precision timedelta values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time. @@ -330,7 +336,7 @@ differences.dataset.diff_temperature.plot() - + @@ -358,7 +364,7 @@ plt.colorbar( label='temperature diff (obs-model)') - + @@ -537,6 +543,9 @@ containing averaged data for each region: mask_means = analysis.mask_means(profile, mask_indices) ``` + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/utils.py:494: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`. + + which can be visualised or further processed @@ -558,7 +567,7 @@ plt.legend() - + @@ -598,7 +607,7 @@ plt.colorbar() - + @@ -639,7 +648,7 @@ plt.colorbar() - + diff --git a/content/en/docs/Examples/Notebooks/Profile/wod_bgc_ragged_example.md b/content/en/docs/Examples/Notebooks/Profile/wod_bgc_ragged_example.md index 04a314f5cd..dedbcd6959 100644 --- a/content/en/docs/Examples/Notebooks/Profile/wod_bgc_ragged_example.md +++ b/content/en/docs/Examples/Notebooks/Profile/wod_bgc_ragged_example.md @@ -128,6 +128,8 @@ model_profiles = wod_profile_sub.obs_operator(seasia_bgc) #model_profiles.dataset # uncomment to print data object summary ``` + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/utils.py:494: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`. + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/utils.py:494: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`. /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/coast/data/profile.py:456: UserWarning: Converting non-nanosecond precision timedelta values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time. /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/coast/data/profile.py:456: UserWarning: Converting non-nanosecond precision timedelta values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time. From 76c5be9cc1f3e2a5d3ffee7c7adb9aa12b8b77b2 Mon Sep 17 00:00:00 2001 From: MarkDownBot Date: Mon, 11 Dec 2023 13:34:20 +0000 Subject: [PATCH 04/10] Updated General notebook pages from coast repo. --- .../Notebooks/General/climatology_tutorial.md | 2 +- .../Notebooks/General/eof_tutorial.md | 4 +- .../General/export_to_netcdf_tutorial.md | 2 +- .../Notebooks/General/mask_maker_tutorial.md | 47 +- .../Notebooks/General/polar_plotting.md | 253 ++++++++++ .../General/seasonal_decomp_example.md | 128 ++--- .../Examples/Notebooks/General/zarr_files.md | 443 ++++++++++++++++++ .../eof_tutorial_files/eof_tutorial_13_1.png | Bin 43922 -> 43022 bytes .../mask_maker_tutorial_18_1.png | Bin 0 -> 26049 bytes .../seasonal_decomp_example_17_1.png | Bin 39094 -> 35842 bytes .../seasonal_decomp_example_7_1.png | Bin 69018 -> 68237 bytes .../seasonal_decomp_example_9_1.png | Bin 33428 -> 30907 bytes 12 files changed, 792 insertions(+), 87 deletions(-) create mode 100644 content/en/docs/Examples/Notebooks/General/polar_plotting.md create mode 100644 content/en/docs/Examples/Notebooks/General/zarr_files.md create mode 100644 static/mask_maker_tutorial_files/mask_maker_tutorial_18_1.png diff --git a/content/en/docs/Examples/Notebooks/General/climatology_tutorial.md b/content/en/docs/Examples/Notebooks/General/climatology_tutorial.md index 54653560bc..e7002c979c 100644 --- a/content/en/docs/Examples/Notebooks/General/climatology_tutorial.md +++ b/content/en/docs/Examples/Notebooks/General/climatology_tutorial.md @@ -70,7 +70,7 @@ nemo_data = coast.Gridded(fn_data=fn_nemo_dat, ``` - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:278: UserWarning: The specified chunks separate the stored chunks along dimension "time_counter" starting at index 2. This could degrade performance. Instead, consider rechunking after loading. + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:282: UserWarning: The specified chunks separate the stored chunks along dimension "time_counter" starting at index 2. This could degrade performance. Instead, consider rechunking after loading. Calculate the climatology for temperature and sea surface height (ssh) as an example: diff --git a/content/en/docs/Examples/Notebooks/General/eof_tutorial.md b/content/en/docs/Examples/Notebooks/General/eof_tutorial.md index 358eaf1105..760ffae037 100644 --- a/content/en/docs/Examples/Notebooks/General/eof_tutorial.md +++ b/content/en/docs/Examples/Notebooks/General/eof_tutorial.md @@ -89,7 +89,7 @@ eof_data.EOF.sel(mode=[1,2,3,4]).plot.pcolormesh(col='mode',col_wrap=2,x='longit - + @@ -107,7 +107,7 @@ eof_data.temporal_proj.sel(mode=[1,2,3,4]).plot(col='mode',col_wrap=2,x='time') - + diff --git a/content/en/docs/Examples/Notebooks/General/export_to_netcdf_tutorial.md b/content/en/docs/Examples/Notebooks/General/export_to_netcdf_tutorial.md index c95ea7cab9..56b5984272 100644 --- a/content/en/docs/Examples/Notebooks/General/export_to_netcdf_tutorial.md +++ b/content/en/docs/Examples/Notebooks/General/export_to_netcdf_tutorial.md @@ -58,7 +58,7 @@ ofile = "example_export_output.nc" # The target filename for output nemo = coast.Gridded(fn_nemo_dat, fn_nemo_dom, config=config) ``` - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:278: UserWarning: The specified chunks separate the stored chunks along dimension "time_counter" starting at index 2. This could degrade performance. Instead, consider rechunking after loading. + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:282: UserWarning: The specified chunks separate the stored chunks along dimension "time_counter" starting at index 2. This could degrade performance. Instead, consider rechunking after loading. ### We can export the whole xr.DataSet to a netCDF file diff --git a/content/en/docs/Examples/Notebooks/General/mask_maker_tutorial.md b/content/en/docs/Examples/Notebooks/General/mask_maker_tutorial.md index 8af1bce45b..2927a3e0ea 100644 --- a/content/en/docs/Examples/Notebooks/General/mask_maker_tutorial.md +++ b/content/en/docs/Examples/Notebooks/General/mask_maker_tutorial.md @@ -161,9 +161,12 @@ mm.quick_plot(mask_xr) ``` + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/utils.py:494: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`. + + -![png](/COAsT/mask_maker_tutorial_files/mask_maker_tutorial_18_0.png) +![png](/COAsT/mask_maker_tutorial_files/mask_maker_tutorial_18_1.png) @@ -198,7 +201,7 @@ mask_xr.mask.sum(dim='dim_mask').plot(levels=(1,2,3,4)) - + @@ -240,6 +243,9 @@ This returns an object called `mask_indices`, which is required to pass to `Prof profile_mask_means = analysis.mask_means(profile, mask_indices) ``` + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/utils.py:494: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`. + + This routine operates over all variables in the `profile` object. It calculates means by region preserving depth information (`profile_mean_*`) and also averaging over depth information (`all_mean_*`). The variables are returned with these prefixes accordingly. @@ -630,9 +636,9 @@ Data variables: all_mean_temperature (dim_mask) float32 7.48 6.685 ... 7.275 all_mean_practical_salinity (dim_mask) float32 34.57 34.86 ... 33.76 all_mean_qc_flags_profiles (dim_mask) float64 4.422e+05 ... 1.93... - all_mean_qc_flags_levels (dim_mask) float64 3.272e+07 ... 3.68...
              • @@ -717,6 +723,9 @@ print(check1) False + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/utils.py:494: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`. + + The mean profiles can be visualised or further processed (notice the Irish Sea region and FSC are missing because there were no profiles in the example dataset) @@ -739,7 +748,7 @@ plt.legend() - + @@ -771,7 +780,7 @@ mask_mean_SST.plot() - + @@ -799,7 +808,7 @@ plt.legend() - + diff --git a/content/en/docs/Examples/Notebooks/General/polar_plotting.md b/content/en/docs/Examples/Notebooks/General/polar_plotting.md new file mode 100644 index 0000000000..ec41a1a147 --- /dev/null +++ b/content/en/docs/Examples/Notebooks/General/polar_plotting.md @@ -0,0 +1,253 @@ +--- + title: "Polar plotting" + linkTitle: "Polar plotting" + weight: 5 + + description: > + Polar plotting example. +--- +# Polar Plotting Tutorial + +This demonstration will show how to re-project the NEMO velocities for quiver plotting in polar coordinates. + +NEMO velocities are usually calculated and saved in along grid i and j directions. This causes an issue when plotting velocities as vectors on a map where it is assumed that i and j velocities point eastwards and northwards but the grid is curvilinear. + +There are additional isses when plotting quivers over the poles that we will cover. + +The data we use here comes from a global model that has been cropped to the Arctic region. If you want to see the images, please go to the configuration gallery codes in `example_scripts/configuration_gallery/polar_plotting.py` or in the [documentation](https://british-oceanographic-data-centre.github.io/COAsT/docs/examples/configs_gallery/). + +**IMPORTANT: you have to install cartopy in order to plot the maps** + + +```python +# !pip install cartopy +``` + + +```python +import matplotlib.pyplot as plt +import cartopy.crs as ccrs +import cartopy.feature as cfeature +import coast +``` + + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pydap/lib.py:5: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap')`. + Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap.responses')`. + Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2350: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap')`. + Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap.handlers')`. + Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2350: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap')`. + Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap.tests')`. + Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2350: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap')`. + Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('sphinxcontrib')`. + Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages + + +### Usage of coast._utils.plot_util.velocity_grid_to_geo() + +Plotting velocities with curvilinear grid and/or on a polar stereographic projection. + + +```python +root = "./" +# Paths to a single or multiple data files. +dn_files = root + "example_files/" +fn_nemo_dat_t = dn_files + "coast_nemo_quiver_thetao.nc" +fn_nemo_dat_u = dn_files + "coast_nemo_quiver_uo.nc" +fn_nemo_dat_v = dn_files + "coast_nemo_quiver_vo.nc" +fn_nemo_config_t = root + "config/gc31_nemo_grid_t.json" +fn_nemo_config_u = root + "config/gc31_nemo_grid_u.json" +fn_nemo_config_v = root + "config/gc31_nemo_grid_v.json" + +# Set path for domain file if required. +fn_nemo_dom = dn_files + "coast_nemo_quiver_dom.nc" + +# Define output filepath (optional: None or str) +fn_out = './quiver_plot.png' +``` + + +```python +# Read in multiyear data (This example uses NEMO data from a single file.) +nemo_data_t = coast.Gridded(fn_data=fn_nemo_dat_t, + fn_domain=fn_nemo_dom, + config=fn_nemo_config_t, + ).dataset +nemo_data_u = coast.Gridded(fn_data=fn_nemo_dat_u, + fn_domain=fn_nemo_dom, + config=fn_nemo_config_u, + ).dataset +nemo_data_v = coast.Gridded(fn_data=fn_nemo_dat_v, + fn_domain=fn_nemo_dom, + config=fn_nemo_config_v, + ).dataset +``` + +Select surface u and v as an example: + + +```python +# Select specific data variables. +data_u = nemo_data_u[["u_velocity"]] +data_v = nemo_data_v[["v_velocity"]] + +# Select one time step and surface currents +data_u = data_u.isel(t_dim=0, z_dim=0) +data_v = data_v.isel(t_dim=0, z_dim=0) + +# Calculate speed +speed = ((data_u.to_array().values[0, :, :] ** 2 + data_v.to_array().values[0, :, :] ** 2) ** 0.5) +``` + +Calculate adjustment for the curvilinear grid + +This function may take a while + + +```python +uv_velocities = [data_u.to_array().values[0, :, :], data_v.to_array().values[0, :, :]] +u_new, v_new = coast._utils.plot_util.velocity_grid_to_geo( + nemo_data_t.longitude.values, nemo_data_t.latitude.values, + uv_velocities, + polar_stereo_cartopy_bug_fix=False) +``` + + 100%|██████████| 59/59 [07:01<00:00, 7.15s/it] + + + +```python +# Apply the CartoPy stereographic polar correction +# NOTE: This could have been applied automatically with `polar_stereo=True` in +# coast._utils.plot_util.velocity_grid_to_geo() + +u_pol, v_pol = coast._utils.plot_util.velocity_polar_bug_fix(u_new, v_new, nemo_data_t.latitude.values) +``` + + +```python +# Set things up for plotting North Pole stereographic projection + +# Data projection +data_crs = ccrs.PlateCarree() +# Plot projection +mrc = ccrs.NorthPolarStereo(central_longitude=0.0) +``` + +Below shows the u and v velocities when plotted with and without adjustment. + +The plot shows three cases: 1:no correction, 2:the NEMO grid correction, and 3:the NEMO grid correction with polar stereographic plot correction. We also plot the final corrected u and v velocities as streamlines. +- In case 1, the lower latitude velocities aren't too bad but become irregular further north as the grid lines deviate form lat and lon. +- In case 2, the irregularity still persists even with the grid correction. This is the result of a CartoPy bug which also worsens at high latitudes. +- In case 3, both corrections have been applied and the velocities quivers now align with the route of strong current speed as would be expected. + +`!WARNING: UNCOMMENT THE LINES BELOW TO PLOT THE MAPS` + + +```python +# # Subplot axes settings +# n_r = 2 # Number of subplot rows +# n_c = 2 # Number of subplot columns +# figsize = (10, 10) # Figure size +# subplot_padding = 0.5 # Amount of vertical and horizontal padding between plots +# fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure padding (left, top, right, bottom) + +# # Labels and Titles +# fig_title = "Velocity Plot" # Whole figure title + +# # Create plot and flatten axis array +# fig, ax = plt.subplots(n_r, n_c, subplot_kw={"projection": mrc}, sharey=True, sharex=True, figsize=figsize) +# cax = fig.add_axes([0.3, 0.96, 0.4, 0.01]) + + +# ax = ax.flatten() +# for rr in range(n_r * n_c): +# ax[rr].add_feature(cfeature.LAND, zorder=100) +# ax[rr].gridlines() +# ax[rr].set_extent([-180, 180, 70, 90], crs=data_crs) +# coast._utils.plot_util.set_circle(ax[rr]) + + +# cs = ax[0].pcolormesh(nemo_data_t.longitude.values, nemo_data_t.latitude.values, speed, transform=data_crs, vmin=0, vmax=0.3) +# ax[0].quiver(nemo_data_t.longitude.values, nemo_data_t.latitude.values, +# data_u.to_array().values[0, :, :], data_v.to_array().values[0, :, :], +# color='w', transform=data_crs, angles='xy', regrid_shape=40) + +# ax[1].pcolormesh(nemo_data_t.longitude.values, nemo_data_t.latitude.values, speed, transform=data_crs, vmin=0, vmax=0.3) +# ax[1].quiver(nemo_data_t.longitude.values, nemo_data_t.latitude.values, +# u_new, v_new, +# color='w', transform=data_crs, angles='xy', regrid_shape=40) + +# ax[2].pcolormesh(nemo_data_t.longitude.values, nemo_data_t.latitude.values, speed, transform=data_crs, vmin=0, vmax=0.3) +# ax[2].quiver(nemo_data_t.longitude.values, nemo_data_t.latitude.values, +# u_pol, v_pol, +# color='w', transform=data_crs, angles='xy', regrid_shape=40) + +# ax[3].pcolormesh(nemo_data_t.longitude.values, nemo_data_t.latitude.values, speed, transform=data_crs, vmin=0, vmax=0.3) +# ax[3].streamplot(nemo_data_t.longitude.values, nemo_data_t.latitude.values, +# u_pol, v_pol, transform=data_crs, linewidth=1, density=2, color='w', zorder=101) + +# ax[0].set_title('1: No Correction') +# ax[1].set_title('2: NEMO Grid Correction') +# ax[2].set_title('3: Grid Correction and Polar Correction') +# ax[3].set_title('As left but with streamlines') + +# fig.colorbar(cs, cax=cax, orientation='horizontal') +# cax.set_xlabel('U (m s$^{-1}$)') + +# #fig.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) +# #fig.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) + +# plt.show() +# # uncomment this line to save an output image +# fig.savefig(fn_out) +``` + +Below shows the temperature plots + + +```python +# Data to plot +data_bathy = nemo_data_t.bathymetry.values +data_temp = nemo_data_t.temperature.isel(t_dim=0, z_dim=0) +``` + +`!WARNING: UNCOMMENT THE LINES BELOW TO PLOT THE MAPS` + + +```python +# figsize = (5, 5) # Figure size +# fig = plt.figure(figsize=figsize) +# ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.75], projection=mrc) +# cax = fig.add_axes([0.3, 0.96, 0.4, 0.01]) + +# ax1.add_feature(cfeature.LAND, zorder=105) +# ax1.gridlines() +# ax1.set_extent([-180, 180, 70, 90], crs=data_crs) +# coast._utils.plot_util.set_circle(ax1) + +# # We use to a function to re-project the data for plotting contours over the pole. +# cs1 = coast._utils.plot_util.plot_polar_contour( +# nemo_data_t.longitude.values, nemo_data_t.latitude.values, data_bathy, ax1, levels=6, colors="k", zorder=101 +# ) +# cs2 = ax1.pcolormesh( +# nemo_data_t.longitude.values, nemo_data_t.latitude.values, data_temp, transform=data_crs, vmin=-2, vmax=8 +# ) +# cax.set_xlabel(r"SST ($^{\circ}$C)") +# fig.colorbar(cs2, cax=cax, orientation="horizontal") + +# # fig.savefig(fn_out, dpi=120) + +``` + + +```python + +``` diff --git a/content/en/docs/Examples/Notebooks/General/seasonal_decomp_example.md b/content/en/docs/Examples/Notebooks/General/seasonal_decomp_example.md index e1202849ea..7fdadfb90b 100644 --- a/content/en/docs/Examples/Notebooks/General/seasonal_decomp_example.md +++ b/content/en/docs/Examples/Notebooks/General/seasonal_decomp_example.md @@ -59,7 +59,7 @@ grd = coast.Gridded(fn_nemo_dat, fn_nemo_dom, config=config) Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('sphinxcontrib')`. Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:278: UserWarning: The specified chunks separate the stored chunks along dimension "time_counter" starting at index 2. This could degrade performance. Instead, consider rechunking after loading. + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:282: UserWarning: The specified chunks separate the stored chunks along dimension "time_counter" starting at index 2. This could degrade performance. Instead, consider rechunking after loading. The loaded example data only has 7 time stamps, the code below creates a new (fake) extended temperature variable with 48 monthly records. This code is not required to use the function, it is only included here to make a set of time series that are long enough to be interesting. @@ -91,7 +91,7 @@ temperature = xr.DataArray( ) ``` - /tmp/ipykernel_2964/2929135463.py:14: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time. + /tmp/ipykernel_3938/2929135463.py:14: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time. Check out the new data @@ -109,7 +109,7 @@ temperature[0,0,:,:].plot() - + @@ -130,7 +130,7 @@ temperature[:,0,200,200].plot() - [] + [] @@ -554,7 +554,7 @@ Dimensions without coordinates: z_dim, y_dim, x_dim Data variables: trend (t_dim, z_dim, y_dim, x_dim) float64 nan nan nan ... nan nan nan seasonal (t_dim, z_dim, y_dim, x_dim) float64 nan nan nan ... nan nan nan - residual (t_dim, z_dim, y_dim, x_dim) float64 nan nan nan ... nan nan nan
              • @@ -779,9 +779,9 @@ temp_decomp[:,:,0,200,200].plot(hue="component") - [, - , - ] + [, + , + ] diff --git a/content/en/docs/Examples/Notebooks/General/zarr_files.md b/content/en/docs/Examples/Notebooks/General/zarr_files.md new file mode 100644 index 0000000000..d85727e01e --- /dev/null +++ b/content/en/docs/Examples/Notebooks/General/zarr_files.md @@ -0,0 +1,443 @@ +--- + title: "Zarr files" + linkTitle: "Zarr files" + weight: 5 + + description: > + Zarr files example. +--- +# Load Zarr files + +This tutorial will show some examples on how to load zarr files on coast. +It will include: +- Creation of a Gridded object +- Loading data into the Gridded object. +- Combining Gridded output and Gridded domain data. +- Interrogating the Gridded object. +- Basic manipulation and subsetting +- Looking at the data with matplotlib + +### Requirements + +Coast also has the capability to allow you to open zarr files In order to do that, you need to install first the library zarr: + +`pip install zarr` + +After that, you can open the datasets + +### Import + +Begin by importing COAsT and define some file paths for NEMO output data and a NEMO domain, as an example of model data suitable for the Gridded object. + + +```python +import coast +import matplotlib.pyplot as plt +import datetime +import numpy as np +import xarray as xr + +root = "./" +fn_config_t_grid = root + "./config/example_nemo_monthly_climate.json" + +# Define some file paths +fn_nemo_dom_mask = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/mask.zarr" +fn_nemo_dom_mesh_zgr = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/mesh_zgr.zarr" +fn_nemo_dom_mesh_hgr = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/mesh_hgr.zarr" +fn_nemo_dat_t = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/n06_T.zarr" +fn_nemo_dat_u = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/n06_U.zarr" +fn_nemo_dat_v = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/n06_V.zarr" + +``` + + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pydap/lib.py:5: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap')`. + Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap.responses')`. + Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2350: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap')`. + Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap.handlers')`. + Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2350: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap')`. + Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap.tests')`. + Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2350: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap')`. + Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('sphinxcontrib')`. + Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages + + +### Open the zarr files as a XARRAY + +The zarr files that we are using in this example do not have all the variables on the same file. Because of that, we need to open each file separately and then add the variables to a central file + + +```python +dom = xr.open_zarr(fn_nemo_dom_mask) +mesh_zgr = xr.open_zarr(fn_nemo_dom_mesh_zgr) +mesh_hgr = xr.open_zarr(fn_nemo_dom_mesh_hgr) +``` + + + --------------------------------------------------------------------------- + + ValueError Traceback (most recent call last) + + Cell In[2], line 1 + ----> 1 dom = xr.open_zarr(fn_nemo_dom_mask) + 2 mesh_zgr = xr.open_zarr(fn_nemo_dom_mesh_zgr) + 3 mesh_hgr = xr.open_zarr(fn_nemo_dom_mesh_hgr) + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/backends/zarr.py:944, in open_zarr(store, group, synchronizer, chunks, decode_cf, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, consolidated, overwrite_encoded_chunks, chunk_store, storage_options, decode_timedelta, use_cftime, zarr_version, chunked_array_type, from_array_kwargs, **kwargs) + 930 raise TypeError( + 931 "open_zarr() got unexpected keyword arguments " + ",".join(kwargs.keys()) + 932 ) + 934 backend_kwargs = { + 935 "synchronizer": synchronizer, + 936 "consolidated": consolidated, + (...) + 941 "zarr_version": zarr_version, + 942 } + --> 944 ds = open_dataset( + 945 filename_or_obj=store, + 946 group=group, + 947 decode_cf=decode_cf, + 948 mask_and_scale=mask_and_scale, + 949 decode_times=decode_times, + 950 concat_characters=concat_characters, + 951 decode_coords=decode_coords, + 952 engine="zarr", + 953 chunks=chunks, + 954 drop_variables=drop_variables, + 955 chunked_array_type=chunked_array_type, + 956 from_array_kwargs=from_array_kwargs, + 957 backend_kwargs=backend_kwargs, + 958 decode_timedelta=decode_timedelta, + 959 use_cftime=use_cftime, + 960 zarr_version=zarr_version, + 961 ) + 962 return ds + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/backends/api.py:558, in open_dataset(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs) + 555 if from_array_kwargs is None: + 556 from_array_kwargs = {} + --> 558 backend = plugins.get_backend(engine) + 560 decoders = _resolve_decoders_kwargs( + 561 decode_cf, + 562 open_backend_dataset_parameters=backend.open_dataset_parameters, + (...) + 568 decode_coords=decode_coords, + 569 ) + 571 overwrite_encoded_chunks = kwargs.pop("overwrite_encoded_chunks", None) + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/backends/plugins.py:205, in get_backend(engine) + 203 engines = list_engines() + 204 if engine not in engines: + --> 205 raise ValueError( + 206 f"unrecognized engine {engine} must be one of: {list(engines)}" + 207 ) + 208 backend = engines[engine] + 209 elif isinstance(engine, type) and issubclass(engine, BackendEntrypoint): + + + ValueError: unrecognized engine zarr must be one of: ['netcdf4', 'scipy', 'pydap', 'store'] + + + +```python +for var_name in mesh_zgr.data_vars: + dom[var_name] = mesh_zgr[var_name] +for var_name in mesh_hgr.data_vars: + dom[var_name] = mesh_hgr[var_name] +``` + + + --------------------------------------------------------------------------- + + NameError Traceback (most recent call last) + + Cell In[3], line 1 + ----> 1 for var_name in mesh_zgr.data_vars: + 2 dom[var_name] = mesh_zgr[var_name] + 3 for var_name in mesh_hgr.data_vars: + + + NameError: name 'mesh_zgr' is not defined + + + +```python +u_grid = xr.open_zarr(fn_nemo_dat_u) +u_grid = u_grid.isel(time_counter=slice(0,119)).rename({'depthu': 'depth'}) +v_grid = xr.open_zarr(fn_nemo_dat_v) +v_grid = v_grid.isel(time_counter=slice(0,119)).rename({'depthv': 'depth'}) +t_grid = xr.open_zarr(fn_nemo_dat_t) +t_grid = t_grid.rename({'deptht': 'depth'}) +``` + + + --------------------------------------------------------------------------- + + ValueError Traceback (most recent call last) + + Cell In[4], line 1 + ----> 1 u_grid = xr.open_zarr(fn_nemo_dat_u) + 2 u_grid = u_grid.isel(time_counter=slice(0,119)).rename({'depthu': 'depth'}) + 3 v_grid = xr.open_zarr(fn_nemo_dat_v) + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/backends/zarr.py:944, in open_zarr(store, group, synchronizer, chunks, decode_cf, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, consolidated, overwrite_encoded_chunks, chunk_store, storage_options, decode_timedelta, use_cftime, zarr_version, chunked_array_type, from_array_kwargs, **kwargs) + 930 raise TypeError( + 931 "open_zarr() got unexpected keyword arguments " + ",".join(kwargs.keys()) + 932 ) + 934 backend_kwargs = { + 935 "synchronizer": synchronizer, + 936 "consolidated": consolidated, + (...) + 941 "zarr_version": zarr_version, + 942 } + --> 944 ds = open_dataset( + 945 filename_or_obj=store, + 946 group=group, + 947 decode_cf=decode_cf, + 948 mask_and_scale=mask_and_scale, + 949 decode_times=decode_times, + 950 concat_characters=concat_characters, + 951 decode_coords=decode_coords, + 952 engine="zarr", + 953 chunks=chunks, + 954 drop_variables=drop_variables, + 955 chunked_array_type=chunked_array_type, + 956 from_array_kwargs=from_array_kwargs, + 957 backend_kwargs=backend_kwargs, + 958 decode_timedelta=decode_timedelta, + 959 use_cftime=use_cftime, + 960 zarr_version=zarr_version, + 961 ) + 962 return ds + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/backends/api.py:558, in open_dataset(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs) + 555 if from_array_kwargs is None: + 556 from_array_kwargs = {} + --> 558 backend = plugins.get_backend(engine) + 560 decoders = _resolve_decoders_kwargs( + 561 decode_cf, + 562 open_backend_dataset_parameters=backend.open_dataset_parameters, + (...) + 568 decode_coords=decode_coords, + 569 ) + 571 overwrite_encoded_chunks = kwargs.pop("overwrite_encoded_chunks", None) + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/backends/plugins.py:205, in get_backend(engine) + 203 engines = list_engines() + 204 if engine not in engines: + --> 205 raise ValueError( + 206 f"unrecognized engine {engine} must be one of: {list(engines)}" + 207 ) + 208 backend = engines[engine] + 209 elif isinstance(engine, type) and issubclass(engine, BackendEntrypoint): + + + ValueError: unrecognized engine zarr must be one of: ['netcdf4', 'scipy', 'pydap', 'store'] + + + +```python +for var_name in u_grid.data_vars: + t_grid[var_name] = u_grid[var_name] +for var_name in v_grid.data_vars: + t_grid[var_name] = v_grid[var_name] +``` + + + --------------------------------------------------------------------------- + + NameError Traceback (most recent call last) + + Cell In[5], line 1 + ----> 1 for var_name in u_grid.data_vars: + 2 t_grid[var_name] = u_grid[var_name] + 3 for var_name in v_grid.data_vars: + + + NameError: name 'u_grid' is not defined + + + +```python +dom +``` + + + --------------------------------------------------------------------------- + + NameError Traceback (most recent call last) + + Cell In[6], line 1 + ----> 1 dom + + + NameError: name 'dom' is not defined + + + +```python +t_grid +``` + + + --------------------------------------------------------------------------- + + NameError Traceback (most recent call last) + + Cell In[7], line 1 + ----> 1 t_grid + + + NameError: name 't_grid' is not defined + + +### Slice the zarr files + +Because zarr files are optimized for cloud, when we instantiate an xarray dataset, we do not open the zarr by it self. We only open some metadata related to the file. The files will only be downloaed when we need to perform some processing on the data + + +```python +dom = dom.isel(y=slice(500, 700), x=slice(1000,1200)) +``` + + + --------------------------------------------------------------------------- + + NameError Traceback (most recent call last) + + Cell In[8], line 1 + ----> 1 dom = dom.isel(y=slice(500, 700), x=slice(1000,1200)) + + + NameError: name 'dom' is not defined + + + +```python +t_grid = t_grid.isel(y=slice(500, 700), x=slice(1000,1200), time_counter=slice(0,24)) +``` + + + --------------------------------------------------------------------------- + + NameError Traceback (most recent call last) + + Cell In[9], line 1 + ----> 1 t_grid = t_grid.isel(y=slice(500, 700), x=slice(1000,1200), time_counter=slice(0,24)) + + + NameError: name 't_grid' is not defined + + +### Loading and Interrogating + +We can create a new Gridded object by simple calling `coast.Gridded()`. By passing this a NEMO data file and a NEMO domain file, COAsT will combine the two into a single xarray dataset within the Gridded object. Each individual Gridded object should be for a specified NEMO grid type, which is specified in a configuration file which is also passed as an argument. The Dask library is switched on by default, chunking can be specified in the configuration file. + + +```python +nemo_t = coast.Gridded(fn_data= t_grid, fn_domain = dom, config=fn_config_t_grid) +``` + + + --------------------------------------------------------------------------- + + NameError Traceback (most recent call last) + + Cell In[10], line 1 + ----> 1 nemo_t = coast.Gridded(fn_data= t_grid, fn_domain = dom, config=fn_config_t_grid) + + + NameError: name 't_grid' is not defined + + +`!IMPORTANT!`: this dataset does not contain bathymetry data. + +Our new Gridded object `nemo_t` contains a variable called dataset, which holds information on the two files we passed. Let’s have a look at this: + + +```python +# nemo_t.dataset # uncomment to print data object summary +``` + +This is an xarray dataset, which has all the information on netCDF style structures. You can see dimensions, coordinates and data variables. At the moment, none of the actual data is loaded to memory and will remain that way until it needs to be accessed. + +As it is a zarr file, it will only get any data if you apply `compute()` on the data: + + +```python +ssh = nemo_t.dataset.ssh +ssh.compute() +# ssh # uncomment to print data object summary +``` + + + --------------------------------------------------------------------------- + + NameError Traceback (most recent call last) + + Cell In[12], line 1 + ----> 1 ssh = nemo_t.dataset.ssh + 2 ssh.compute() + 3 # ssh # uncomment to print data object summary + + + NameError: name 'nemo_t' is not defined + + +Or as a numpy array: + + +```python +ssh_np = ssh.values +#ssh_np.shape # uncomment to print data object summary +``` + + + --------------------------------------------------------------------------- + + NameError Traceback (most recent call last) + + Cell In[13], line 1 + ----> 1 ssh_np = ssh.values + 2 #ssh_np.shape # uncomment to print data object summary + + + NameError: name 'ssh' is not defined + + +Then lets plot up a single time snapshot of ssh using matplotlib: + + +```python +plt.pcolormesh(nemo_t.dataset.longitude, nemo_t.dataset.latitude, nemo_t.dataset.ssh[0]) +``` + + + --------------------------------------------------------------------------- + + NameError Traceback (most recent call last) + + Cell In[14], line 1 + ----> 1 plt.pcolormesh(nemo_t.dataset.longitude, nemo_t.dataset.latitude, nemo_t.dataset.ssh[0]) + + + NameError: name 'nemo_t' is not defined + + + +```python + +``` diff --git a/static/eof_tutorial_files/eof_tutorial_13_1.png b/static/eof_tutorial_files/eof_tutorial_13_1.png index 3a4f5528851f475fd0bc57157d7d77ab38d87252..72338838c7970feba4cb001a955489d51791baef 100644 GIT binary patch literal 43022 zcmb5Wby!vF7e9Ci0qIbXmQ=c=K@<)l(%l`>-5@0;B_bsP0xBXP-Q6H1N+TtLbf|>n ztmFOuX6DZspX+ml1AD*wjkVS%){aqAkte{V!bKnu1P>ID8VCe>1^iXP!GiBRWc%?Q z{wL}wtLLfdV&my!?rx1xHurRObn$ewv$*MP?e1ac;w-=|!p+Zl^O2{gtA`j5kJJDC 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                • @@ -951,7 +951,7 @@ Attributes: end_date: 31oct2020-23.45.00 contributor: national_oceanography_centre,_liverpool datum_information: the_data_refer_to_admiralty_chart_datum_(acd) - parameter_code: aslvbg02_=_surface_elevation_(unspecified_datum)_of_t...
                  • port :
                    p234
                    site :
                    liverpool,_gladstone_dock
                    start_date :
                    01oct2020-00.00.00
                    end_date :
                    31oct2020-23.45.00
                    contributor :
                    national_oceanography_centre,_liverpool
                    datum_information :
                    the_data_refer_to_admiralty_chart_datum_(acd)
                    parameter_code :
                    aslvbg02_=_surface_elevation_(unspecified_datum)_of_the_water_body_by_bubbler_tide_gauge_(second_sensor)
                  • @@ -1089,7 +1089,7 @@ tt = xr.open_dataset(fn_tg) obs = coast.Tidegauge(dataset=tt) ``` - Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x55a7d8e51980 initialised @@ -1100,7 +1100,7 @@ obs = coast.Tidegauge(dataset=tt) obs.dataset = obs.dataset.set_coords("time") ``` - Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x55a7d8e51980 initialised ## Quick plotting to visualise Tidegauge data @@ -1168,7 +1168,7 @@ obs.isel(id_dim=stn_id).plot_timeseries(date_start=np.datetime64('2007-01-01'), (
                    , - ) + ) @@ -1200,9 +1200,9 @@ obs_lower = obs_cut.subset_indices_lonlat_box(lonbounds = [-9, -3], fig, ax = obs_upper.plot_on_map_multiple([obs_upper, obs_lower], color_var_str="latitude") ``` - Tidegauge object at 0x5627e1b35980 initialised - Tidegauge object at 0x5627e1b35980 initialised - Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x55a7d8e51980 initialised + Tidegauge object at 0x55a7d8e51980 initialised + Tidegauge object at 0x55a7d8e51980 initialised @@ -1256,7 +1256,7 @@ tidegauge_from_model = obs.obs_operator(nemo, time_interp='nearest') Indexing model data at tide gauge locations.. Calculating interpolation distances. Interpolating in time... - Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x55a7d8e51980 initialised However, the new `tidegauge_from_model` will the same number of time entries as the `obs` data, rather than the model data (so this will include lots of empty values). So, for a more useful demonstration we trim the observed gauge data so it better matches the model data. @@ -1269,7 +1269,7 @@ end_date = datetime.datetime(2007, 1, 31) obs = obs.time_slice(start_date, end_date) ``` - Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x55a7d8e51980 initialised ### Interpolate model data onto obs locations @@ -1287,7 +1287,7 @@ model_timeseries.dataset Indexing model data at tide gauge locations.. Calculating interpolation distances. Interpolating in time... - Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x55a7d8e51980 initialised @@ -1683,7 +1683,7 @@ Attributes: timeStamp: 2019-Dec-26 04:35:28 GMT uuid: 96cae459-d3a1-4f4f-b82b-9259179f95f7 history: Tue May 19 12:07:51 2020: ncks -v votemper,sossheig -d time... - NCO: 4.4.7
                  • name :
                    AMM7_1d_20070101_20070131_25hourm_grid_T
                    description :
                    ocean T grid variables, 25h meaned
                    title :
                    ocean T grid variables, 25h meaned
                    Conventions :
                    CF-1.6
                    timeStamp :
                    2019-Dec-26 04:35:28 GMT
                    uuid :
                    96cae459-d3a1-4f4f-b82b-9259179f95f7
                    history :
                    Tue May 19 12:07:51 2020: ncks -v votemper,sossheig -d time_counter,0,30,5 AMM7_1d_20070101_20070131_25hourm_grid_T.nc example_data.nc
                    NCO :
                    4.4.7
                  • @@ -1932,8 +1932,8 @@ tganalysis = coast.TidegaugeAnalysis() obs_new, model_new = tganalysis.match_missing_values(obs.dataset.ssh, model_timeseries.dataset.ssh) ``` - Tidegauge object at 0x5627e1b35980 initialised - Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x55a7d8e51980 initialised + Tidegauge object at 0x55a7d8e51980 initialised Although we input data arrays to the above routine, it returns two new Tidegauge objects. Now you have equivalent and comparable sets of time series that can be easily compared. @@ -1959,8 +1959,8 @@ model_new = tganalysis.demean_timeseries(model_new.dataset) # easily compared. ``` - Tidegauge object at 0x5627e1b35980 initialised - Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x55a7d8e51980 initialised + Tidegauge object at 0x55a7d8e51980 initialised @@ -2166,7 +2166,7 @@ tide_obs = tganalysis.reconstruct_tide_utide(obs_new.dataset.time, ha_obs) prep/calcs ... done. prep/calcs ... done. prep/calcs ... done. - Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x55a7d8e51980 initialised prep/calcs ... done. prep/calcs ... done. prep/calcs ... done. @@ -2216,7 +2216,7 @@ tide_obs = tganalysis.reconstruct_tide_utide(obs_new.dataset.time, ha_obs) prep/calcs ... done. prep/calcs ... done. prep/calcs ... done. - Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x55a7d8e51980 initialised Get new TidegaugeMultiple objects containing non tidal residuals: @@ -2230,8 +2230,8 @@ ntr_obs = tganalysis.calculate_non_tidal_residuals(obs_new.dataset.ssh, tide_obs ntr_obs.dataset ``` - Tidegauge object at 0x5627e1b35980 initialised - Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x55a7d8e51980 initialised + Tidegauge object at 0x55a7d8e51980 initialised @@ -2609,7 +2609,7 @@ Coordinates: site_name (id_dim) <U25 'Harwich' 'Mumbles' 'Ullapool' ... 'N/A' 'N/A' Dimensions without coordinates: t_dim, id_dim Data variables: - ntr (id_dim, t_dim) float64 0.4182 0.4182 0.4182 ... -0.02699 0.2945
                    • @@ -2720,7 +2720,7 @@ dx0.dataset ``` - Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x55a7d8e51980 initialised @@ -3098,7 +3098,7 @@ Coordinates: Dimensions without coordinates: t_dim, id_dim Data variables: time (t_dim) datetime64[ns] 2007-01-01 ... 2007-01-30T23:00:00 - ssh (id_dim, t_dim) float64 nan nan nan nan nan ... nan nan nan nan
                      • @@ -3635,7 +3635,7 @@ Data variables: peak_count_ntr (id_dim, threshold) float64 40.0 40.0 ... 2.0 1.0 time_over_threshold_ntr (id_dim, threshold) float64 720.0 720.0 ... 5.0 4.0 dailymax_count_ntr (id_dim, threshold) float64 30.0 30.0 ... 2.0 1.0 - monthlymax_count_ntr (id_dim, threshold) float64 1.0 1.0 1.0 ... 1.0 1.0
                      • @@ -3805,7 +3805,7 @@ plt.plot(thresh_obs.threshold[22], normalised_event_count[22], 'r+', markersize= - [] + [] @@ -3831,8 +3831,8 @@ ssh_diff = tganalysis.difference(obs_new.dataset, model_new.dataset) ntr_diff.dataset ``` - Tidegauge object at 0x5627e1b35980 initialised - Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x55a7d8e51980 initialised + Tidegauge object at 0x55a7d8e51980 initialised @@ -4212,10 +4212,10 @@ Dimensions without coordinates: t_dim, id_dim Data variables: diff_ntr (id_dim, t_dim) float64 nan nan nan ... 0.05885 0.3435 abs_diff_ntr (id_dim, t_dim) float64 nan nan nan ... 0.05885 0.3435 - square_diff_ntr (id_dim, t_dim) float64 nan nan nan ... 0.003464 0.118
                      • diff --git a/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md b/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md index 87dc52dd38..15317e7b63 100644 --- a/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md @@ -72,7 +72,7 @@ Initiate a TideGauge object, if a filename is passed it assumes it is a GESLA ty tg = coast.Tidegauge() ``` - Tidegauge object at 0x555cb5b8d980 initialised + Tidegauge object at 0x556642be7980 initialised Specify the data read as a High Low Water dataset. @@ -103,7 +103,7 @@ tg.dataset.plot.scatter(x="time", y="ssh") - + From 9473799d03287f1bc83c7aa9d730d4553133bfa5 Mon Sep 17 00:00:00 2001 From: MarkDownBot Date: Mon, 11 Dec 2023 15:35:57 +0000 Subject: [PATCH 08/10] Updated Gridded notebook pages from coast repo. --- .../Notebooks/Gridded/introduction_to_gridded_class.md | 4 ++-- .../Notebooks/Gridded/potential_energy_tutorial.md | 10 +++++----- .../Gridded/seasia_dic_example_plot_tutorial.md | 2 +- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/content/en/docs/Examples/Notebooks/Gridded/introduction_to_gridded_class.md b/content/en/docs/Examples/Notebooks/Gridded/introduction_to_gridded_class.md index 0daa3607af..d55b8222b1 100644 --- a/content/en/docs/Examples/Notebooks/Gridded/introduction_to_gridded_class.md +++ b/content/en/docs/Examples/Notebooks/Gridded/introduction_to_gridded_class.md @@ -109,7 +109,7 @@ plt.pcolormesh(nemo_t.dataset.longitude, nemo_t.dataset.latitude, nemo_t.dataset - + @@ -183,7 +183,7 @@ plt.ylabel("latitude") plt.show() ``` - /tmp/ipykernel_2729/2498690501.py:2: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh. + /tmp/ipykernel_2627/2498690501.py:2: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh. diff --git a/content/en/docs/Examples/Notebooks/Gridded/potential_energy_tutorial.md b/content/en/docs/Examples/Notebooks/Gridded/potential_energy_tutorial.md index 262090057f..6c831a43b9 100644 --- a/content/en/docs/Examples/Notebooks/Gridded/potential_energy_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Gridded/potential_energy_tutorial.md @@ -489,10 +489,10 @@ Coordinates: longitude (y_dim, x_dim) float32 -19.89 -19.78 -19.67 ... 12.78 12.89 13.0 Dimensions without coordinates: t_dim, y_dim, x_dim Data variables: - PEA (t_dim, y_dim, x_dim) float64 nan nan nan nan ... nan nan nan nan
                        • diff --git a/content/en/docs/Examples/Notebooks/Gridded/seasia_dic_example_plot_tutorial.md b/content/en/docs/Examples/Notebooks/Gridded/seasia_dic_example_plot_tutorial.md index 1fecd041d5..6e2985753d 100644 --- a/content/en/docs/Examples/Notebooks/Gridded/seasia_dic_example_plot_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Gridded/seasia_dic_example_plot_tutorial.md @@ -75,7 +75,7 @@ plt.ylabel("latitude") plt.show() ``` - /tmp/ipykernel_2849/1161426776.py:2: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh. + /tmp/ipykernel_2746/1161426776.py:2: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh. From c5ef078e9a165b9640731971bd898b0a8ede6621 Mon Sep 17 00:00:00 2001 From: MarkDownBot Date: Mon, 11 Dec 2023 15:43:05 +0000 Subject: [PATCH 09/10] Updated General notebook pages from coast repo. --- .../Notebooks/General/eof_tutorial.md | 4 +- .../Notebooks/General/mask_maker_tutorial.md | 36 ++--- .../Notebooks/General/polar_plotting.md | 2 +- .../General/seasonal_decomp_example.md | 66 ++++----- .../Examples/Notebooks/General/zarr_files.md | 135 +++--------------- .../seasonal_decomp_example_17_1.png | Bin 35842 -> 36297 bytes .../seasonal_decomp_example_7_1.png | Bin 68237 -> 68641 bytes .../seasonal_decomp_example_9_1.png | Bin 30907 -> 30560 bytes 8 files changed, 76 insertions(+), 167 deletions(-) diff --git a/content/en/docs/Examples/Notebooks/General/eof_tutorial.md b/content/en/docs/Examples/Notebooks/General/eof_tutorial.md index 760ffae037..2453a1de9c 100644 --- a/content/en/docs/Examples/Notebooks/General/eof_tutorial.md +++ b/content/en/docs/Examples/Notebooks/General/eof_tutorial.md @@ -89,7 +89,7 @@ eof_data.EOF.sel(mode=[1,2,3,4]).plot.pcolormesh(col='mode',col_wrap=2,x='longit - + @@ -107,7 +107,7 @@ eof_data.temporal_proj.sel(mode=[1,2,3,4]).plot(col='mode',col_wrap=2,x='time') - + diff --git a/content/en/docs/Examples/Notebooks/General/mask_maker_tutorial.md b/content/en/docs/Examples/Notebooks/General/mask_maker_tutorial.md index 2927a3e0ea..9be6e9fc88 100644 --- a/content/en/docs/Examples/Notebooks/General/mask_maker_tutorial.md +++ b/content/en/docs/Examples/Notebooks/General/mask_maker_tutorial.md @@ -201,7 +201,7 @@ mask_xr.mask.sum(dim='dim_mask').plot(levels=(1,2,3,4)) - + @@ -636,9 +636,9 @@ Data variables: all_mean_temperature (dim_mask) float32 7.48 6.685 ... 7.275 all_mean_practical_salinity (dim_mask) float32 34.57 34.86 ... 33.76 all_mean_qc_flags_profiles (dim_mask) float64 4.422e+05 ... 1.93... - all_mean_qc_flags_levels (dim_mask) float64 3.272e+07 ... 3.68...
                          • @@ -748,7 +748,7 @@ plt.legend() - + @@ -780,7 +780,7 @@ mask_mean_SST.plot() - + @@ -808,7 +808,7 @@ plt.legend() - + diff --git a/content/en/docs/Examples/Notebooks/General/polar_plotting.md b/content/en/docs/Examples/Notebooks/General/polar_plotting.md index ec41a1a147..c901533da7 100644 --- a/content/en/docs/Examples/Notebooks/General/polar_plotting.md +++ b/content/en/docs/Examples/Notebooks/General/polar_plotting.md @@ -119,7 +119,7 @@ u_new, v_new = coast._utils.plot_util.velocity_grid_to_geo( polar_stereo_cartopy_bug_fix=False) ``` - 100%|██████████| 59/59 [07:01<00:00, 7.15s/it] + 100%|██████████| 59/59 [06:59<00:00, 7.11s/it] diff --git a/content/en/docs/Examples/Notebooks/General/seasonal_decomp_example.md b/content/en/docs/Examples/Notebooks/General/seasonal_decomp_example.md index 7fdadfb90b..853a314214 100644 --- a/content/en/docs/Examples/Notebooks/General/seasonal_decomp_example.md +++ b/content/en/docs/Examples/Notebooks/General/seasonal_decomp_example.md @@ -91,7 +91,7 @@ temperature = xr.DataArray( ) ``` - /tmp/ipykernel_3938/2929135463.py:14: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time. + /tmp/ipykernel_3937/2929135463.py:14: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time. Check out the new data @@ -109,7 +109,7 @@ temperature[0,0,:,:].plot() - + @@ -130,7 +130,7 @@ temperature[:,0,200,200].plot() - [] + [] @@ -554,7 +554,7 @@ Dimensions without coordinates: z_dim, y_dim, x_dim Data variables: trend (t_dim, z_dim, y_dim, x_dim) float64 nan nan nan ... nan nan nan seasonal (t_dim, z_dim, y_dim, x_dim) float64 nan nan nan ... nan nan nan - residual (t_dim, z_dim, y_dim, x_dim) float64 nan nan nan ... nan nan nan
                          • @@ -779,9 +779,9 @@ temp_decomp[:,:,0,200,200].plot(hue="component") - [, - , - ] + [, + , + ] diff --git a/content/en/docs/Examples/Notebooks/General/zarr_files.md b/content/en/docs/Examples/Notebooks/General/zarr_files.md index d85727e01e..5217b7af0e 100644 --- a/content/en/docs/Examples/Notebooks/General/zarr_files.md +++ b/content/en/docs/Examples/Notebooks/General/zarr_files.md @@ -21,7 +21,7 @@ It will include: Coast also has the capability to allow you to open zarr files In order to do that, you need to install first the library zarr: -`pip install zarr` +`pip install zarr xarray[complete] aiohttp requests` After that, you can open the datasets @@ -36,6 +36,7 @@ import matplotlib.pyplot as plt import datetime import numpy as np import xarray as xr +import zarr root = "./" fn_config_t_grid = root + "./config/example_nemo_monthly_climate.json" @@ -69,6 +70,22 @@ fn_nemo_dat_v = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/n06_V.za Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages + + --------------------------------------------------------------------------- + + ModuleNotFoundError Traceback (most recent call last) + + Cell In[1], line 6 + 4 import numpy as np + 5 import xarray as xr + ----> 6 import zarr + 8 root = "./" + 9 fn_config_t_grid = root + "./config/example_nemo_monthly_climate.json" + + + ModuleNotFoundError: No module named 'zarr' + + ### Open the zarr files as a XARRAY The zarr files that we are using in this example do not have all the variables on the same file. Because of that, we need to open each file separately and then add the variables to a central file @@ -83,7 +100,7 @@ mesh_hgr = xr.open_zarr(fn_nemo_dom_mesh_hgr) --------------------------------------------------------------------------- - ValueError Traceback (most recent call last) + NameError Traceback (most recent call last) Cell In[2], line 1 ----> 1 dom = xr.open_zarr(fn_nemo_dom_mask) @@ -91,61 +108,7 @@ mesh_hgr = xr.open_zarr(fn_nemo_dom_mesh_hgr) 3 mesh_hgr = xr.open_zarr(fn_nemo_dom_mesh_hgr) - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/backends/zarr.py:944, in open_zarr(store, group, synchronizer, chunks, decode_cf, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, consolidated, overwrite_encoded_chunks, chunk_store, storage_options, decode_timedelta, use_cftime, zarr_version, chunked_array_type, from_array_kwargs, **kwargs) - 930 raise TypeError( - 931 "open_zarr() got unexpected keyword arguments " + ",".join(kwargs.keys()) - 932 ) - 934 backend_kwargs = { - 935 "synchronizer": synchronizer, - 936 "consolidated": consolidated, - (...) - 941 "zarr_version": zarr_version, - 942 } - --> 944 ds = open_dataset( - 945 filename_or_obj=store, - 946 group=group, - 947 decode_cf=decode_cf, - 948 mask_and_scale=mask_and_scale, - 949 decode_times=decode_times, - 950 concat_characters=concat_characters, - 951 decode_coords=decode_coords, - 952 engine="zarr", - 953 chunks=chunks, - 954 drop_variables=drop_variables, - 955 chunked_array_type=chunked_array_type, - 956 from_array_kwargs=from_array_kwargs, - 957 backend_kwargs=backend_kwargs, - 958 decode_timedelta=decode_timedelta, - 959 use_cftime=use_cftime, - 960 zarr_version=zarr_version, - 961 ) - 962 return ds - - - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/backends/api.py:558, in open_dataset(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs) - 555 if from_array_kwargs is None: - 556 from_array_kwargs = {} - --> 558 backend = plugins.get_backend(engine) - 560 decoders = _resolve_decoders_kwargs( - 561 decode_cf, - 562 open_backend_dataset_parameters=backend.open_dataset_parameters, - (...) - 568 decode_coords=decode_coords, - 569 ) - 571 overwrite_encoded_chunks = kwargs.pop("overwrite_encoded_chunks", None) - - - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/backends/plugins.py:205, in get_backend(engine) - 203 engines = list_engines() - 204 if engine not in engines: - --> 205 raise ValueError( - 206 f"unrecognized engine {engine} must be one of: {list(engines)}" - 207 ) - 208 backend = engines[engine] - 209 elif isinstance(engine, type) and issubclass(engine, BackendEntrypoint): - - - ValueError: unrecognized engine zarr must be one of: ['netcdf4', 'scipy', 'pydap', 'store'] + NameError: name 'fn_nemo_dom_mask' is not defined @@ -183,7 +146,7 @@ t_grid = t_grid.rename({'deptht': 'depth'}) --------------------------------------------------------------------------- - ValueError Traceback (most recent call last) + NameError Traceback (most recent call last) Cell In[4], line 1 ----> 1 u_grid = xr.open_zarr(fn_nemo_dat_u) @@ -191,61 +154,7 @@ t_grid = t_grid.rename({'deptht': 'depth'}) 3 v_grid = xr.open_zarr(fn_nemo_dat_v) - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/backends/zarr.py:944, in open_zarr(store, group, synchronizer, chunks, decode_cf, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, consolidated, overwrite_encoded_chunks, chunk_store, storage_options, decode_timedelta, use_cftime, zarr_version, chunked_array_type, from_array_kwargs, **kwargs) - 930 raise TypeError( - 931 "open_zarr() got unexpected keyword arguments " + ",".join(kwargs.keys()) - 932 ) - 934 backend_kwargs = { - 935 "synchronizer": synchronizer, - 936 "consolidated": consolidated, - (...) - 941 "zarr_version": zarr_version, - 942 } - --> 944 ds = open_dataset( - 945 filename_or_obj=store, - 946 group=group, - 947 decode_cf=decode_cf, - 948 mask_and_scale=mask_and_scale, - 949 decode_times=decode_times, - 950 concat_characters=concat_characters, - 951 decode_coords=decode_coords, - 952 engine="zarr", - 953 chunks=chunks, - 954 drop_variables=drop_variables, - 955 chunked_array_type=chunked_array_type, - 956 from_array_kwargs=from_array_kwargs, - 957 backend_kwargs=backend_kwargs, - 958 decode_timedelta=decode_timedelta, - 959 use_cftime=use_cftime, - 960 zarr_version=zarr_version, - 961 ) - 962 return ds - - - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/backends/api.py:558, in open_dataset(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs) - 555 if from_array_kwargs is None: - 556 from_array_kwargs = {} - --> 558 backend = plugins.get_backend(engine) - 560 decoders = _resolve_decoders_kwargs( - 561 decode_cf, - 562 open_backend_dataset_parameters=backend.open_dataset_parameters, - (...) - 568 decode_coords=decode_coords, - 569 ) - 571 overwrite_encoded_chunks = kwargs.pop("overwrite_encoded_chunks", None) - - - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/backends/plugins.py:205, in get_backend(engine) - 203 engines = list_engines() - 204 if engine not in engines: - --> 205 raise ValueError( - 206 f"unrecognized engine {engine} must be one of: {list(engines)}" - 207 ) - 208 backend = engines[engine] - 209 elif isinstance(engine, type) and issubclass(engine, BackendEntrypoint): - - - ValueError: unrecognized engine zarr must be one of: ['netcdf4', 'scipy', 'pydap', 'store'] + NameError: name 'fn_nemo_dat_u' is not defined diff 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                            + + + + + + + + + + + + + + +
                            <xarray.DataArray 'ssh' (t_dim: 24, y_dim: 200, x_dim: 200)>
                            +array([[[-1.6210064 , -1.6217471 , -1.6231693 , ..., -1.5842544 ,
                            +         -1.5886359 , -1.5930159 ],
                            +        [-1.6208621 , -1.6217225 , -1.6233234 , ..., -1.5888053 ,
                            +         -1.5935609 , -1.5981766 ],
                            +        [-1.621304  , -1.6223003 , -1.6240481 , ..., -1.5930148 ,
                            +         -1.5981003 , -1.6029042 ],
                            +        ...,
                            +        [-0.98735386, -0.9597808 , -0.931632  , ..., -0.6270069 ,
                            +         -0.6278365 , -0.6290987 ],
                            +        [-1.0102477 , -0.9808094 , -0.9505175 , ..., -0.63027424,
                            +         -0.6316009 , -0.63331974],
                            +        [-1.0307174 , -0.99985355, -0.96790063, ..., -0.63465285,
                            +         -0.6367161 , -0.6389442 ]],
                            +
                            +       [[-1.6141921 , -1.6149583 , -1.6157647 , ..., -1.5382599 ,
                            +         -1.5428585 , -1.5479578 ],
                            +        [-1.6137543 , -1.614481  , -1.6153185 , ..., -1.5421394 ,
                            +         -1.5469443 , -1.5521828 ],
                            +        [-1.6131831 , -1.6138711 , -1.6147525 , ..., -1.5456467 ,
                            +         -1.5506693 , -1.5560651 ],
                            +...
                            +        [-0.5291973 , -0.53182423, -0.537423  , ..., -0.7970734 ,
                            +         -0.8119881 , -0.82499063],
                            +        [-0.53454167, -0.5373199 , -0.54308665, ..., -0.77014893,
                            +         -0.7829233 , -0.7941438 ],
                            +        [-0.53937477, -0.5424393 , -0.5484757 , ..., -0.7462123 ,
                            +         -0.7570849 , -0.76668566]],
                            +
                            +       [[-1.6791433 , -1.6803815 , -1.6808515 , ..., -1.5127497 ,
                            +         -1.5175854 , -1.5226325 ],
                            +        [-1.679447  , -1.6804127 , -1.6806444 , ..., -1.5093309 ,
                            +         -1.5140619 , -1.5190879 ],
                            +        [-1.6797307 , -1.6804144 , -1.6804197 , ..., -1.5054852 ,
                            +         -1.5097626 , -1.5145139 ],
                            +        ...,
                            +        [-0.67837346, -0.6714207 , -0.6632468 , ..., -0.77248365,
                            +         -0.79867   , -0.82295316],
                            +        [-0.6826826 , -0.6778399 , -0.67128325, ..., -0.74587363,
                            +         -0.7708025 , -0.794183  ],
                            +        [-0.68843955, -0.68585885, -0.681205  , ..., -0.71919113,
                            +         -0.7428356 , -0.7651531 ]]], dtype=float32)
                            +Coordinates:
                            +  * time       (t_dim) datetime64[ns] 1960-01-06T12:00:00 ... 1961-12-13T22:1...
                            +    longitude  (y_dim, x_dim) float32 156.2 156.3 156.4 ... 172.7 172.8 172.8
                            +    latitude   (y_dim, x_dim) float32 -63.49 -63.49 -63.49 ... -55.07 -55.07
                            +Dimensions without coordinates: t_dim, y_dim, x_dim
                            +Attributes:
                            +    interval_operation:  300s
                            +    interval_write:      1mo
                            +    long_name:           sea_surface_height_above_geoid
                            +    online_operation:    average
                            +    units:               m
                            - NameError: name 'nemo_t' is not defined Or as a numpy array: @@ -314,19 +698,6 @@ ssh_np = ssh.values #ssh_np.shape # uncomment to print data object summary ``` - - --------------------------------------------------------------------------- - - NameError Traceback (most recent call last) - - Cell In[13], line 1 - ----> 1 ssh_np = ssh.values - 2 #ssh_np.shape # uncomment to print data object summary - - - NameError: name 'ssh' is not defined - - Then lets plot up a single time snapshot of ssh using matplotlib: @@ -335,15 +706,16 @@ plt.pcolormesh(nemo_t.dataset.longitude, nemo_t.dataset.latitude, nemo_t.dataset ``` - --------------------------------------------------------------------------- - NameError Traceback (most recent call last) - Cell In[14], line 1 - ----> 1 plt.pcolormesh(nemo_t.dataset.longitude, nemo_t.dataset.latitude, nemo_t.dataset.ssh[0]) + + + - NameError: name 'nemo_t' is not defined + +![png](/COAsT/zarr_files/zarr_files_33_1.png) + diff --git a/content/en/docs/Examples/configs_gallery/index.md b/content/en/docs/Examples/configs_gallery/index.md index 75e70038bf..55f14c9919 100644 --- a/content/en/docs/Examples/configs_gallery/index.md +++ b/content/en/docs/Examples/configs_gallery/index.md @@ -8,6 +8,178 @@ description: > [example_scripts](https://github.com/British-Oceanographic-Data-Centre/COAsT/tree/master/example_scripts) --- + +# Polar plotting example + +```python +""" +polar_plotting.py + +This demonstration will show how to re-project the NEMO velocities for quiver +plotting in polar coordinates. + +""" + +#%% +import coast +import numpy as np +import xarray as xr +import matplotlib.pyplot as plt +import matplotlib.colors as colors # colormap fiddling + +################################################# +#%% Loading data +################################################# + +import matplotlib.pyplot as plt +import cartopy.crs as ccrs +import cartopy.feature as cfeature +import coast + +################################################# +# Usage of coast._utils.plot_util.velocity_grid_to_geo() +# Plotting velocities with curvilinear grid and/or on a polar stereographic projection. +################################################# + +root = "./" +# Paths to a single or multiple data files. +dn_files = root + "example_files/" +fn_nemo_dat_t = dn_files + "coast_nemo_quiver_thetao.nc" +fn_nemo_dat_u = dn_files + "coast_nemo_quiver_uo.nc" +fn_nemo_dat_v = dn_files + "coast_nemo_quiver_vo.nc" +fn_nemo_config_t = root + "config/gc31_nemo_grid_t.json" +fn_nemo_config_u = root + "config/gc31_nemo_grid_u.json" +fn_nemo_config_v = root + "config/gc31_nemo_grid_v.json" + +# Set path for domain file if required. +fn_nemo_dom = dn_files + "coast_nemo_quiver_dom.nc" + +# Define output filepath (optional: None or str) +fn_out = './quiver_plot.png' + +# Read in multiyear data (This example uses NEMO data from a single file.) +nemo_data_t = coast.Gridded(fn_data=fn_nemo_dat_t, + fn_domain=fn_nemo_dom, + config=fn_nemo_config_t, + ).dataset +nemo_data_u = coast.Gridded(fn_data=fn_nemo_dat_u, + fn_domain=fn_nemo_dom, + config=fn_nemo_config_u, + ).dataset +nemo_data_v = coast.Gridded(fn_data=fn_nemo_dat_v, + fn_domain=fn_nemo_dom, + config=fn_nemo_config_v, + ).dataset + +# Select surface u and v as an example: +data_u = nemo_data_u[["u_velocity"]] +data_v = nemo_data_v[["v_velocity"]] + +# Select one time step and surface currents +data_u = data_u.isel(t_dim=0, z_dim=0) +data_v = data_v.isel(t_dim=0, z_dim=0) + +# Calculate speed +speed = ((data_u.to_array().values[0, :, :] ** 2 + data_v.to_array().values[0, :, :] ** 2) ** 0.5) + +# Calculate adjustment for the curvilinear grid +# This function may take a while +uv_velocities = [data_u.to_array().values[0, :, :], data_v.to_array().values[0, :, :]] +u_new, v_new = coast._utils.plot_util.velocity_grid_to_geo( + nemo_data_t.longitude.values, nemo_data_t.latitude.values, + uv_velocities, + polar_stereo_cartopy_bug_fix=False) + +# Apply the CartoPy stereographic polar correction +# NOTE: This could have been applied automatically with `polar_stereo=True` in +# coast._utils.plot_util.velocity_grid_to_geo() +u_pol, v_pol = coast._utils.plot_util.velocity_polar_bug_fix(u_new, v_new, nemo_data_t.latitude.values) + +# Set things up for plotting North Pole stereographic projection +# Data projection +data_crs = ccrs.PlateCarree() +# Plot projection +mrc = ccrs.NorthPolarStereo(central_longitude=0.0) + +################################################# +# Below shows the u and v velocities when plotted with and without adjustment. +# The plot shows three cases: 1:no correction, 2:the NEMO grid correction, and 3: +# the NEMO grid correction with polar stereographic plot correction. We also plot +# the final corrected u and v velocities as streamlines. +# +# In case 1, the lower latitude velocities aren't too bad but become irregular +# further north as the grid lines deviate form lat and lon. +# +# In case 2, the irregularity still persists even with the grid correction. This +# is the result of a CartoPy bug which also worsens at high latitudes. +# +# In case 3, both corrections have been applied and the velocities quivers now align +# with the route of strong current speed as would be expected. +################################################# + +# Subplot axes settings +n_r = 2 # Number of subplot rows +n_c = 2 # Number of subplot columns +figsize = (20, 20) # Figure size +subplot_padding = 0.5 # Amount of vertical and horizontal padding between plots +fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure padding (left, top, right, bottom) + +# Labels and Titles +fig_title = "Velocity Plot" # Whole figure title + +# Create plot and flatten axis array +fig, ax = plt.subplots(n_r, n_c, subplot_kw={"projection": mrc}, sharey=True, sharex=True, figsize=figsize) +cax = fig.add_axes([0.3, 0.96, 0.4, 0.01]) + + +ax = ax.flatten() +for rr in range(n_r * n_c): + ax[rr].add_feature(cfeature.LAND, zorder=100) + ax[rr].gridlines() + ax[rr].set_extent([-180, 180, 70, 90], crs=data_crs) + coast._utils.plot_util.set_circle(ax[rr]) + + +cs = ax[0].pcolormesh(nemo_data_t.longitude.values, nemo_data_t.latitude.values, speed, transform=data_crs, vmin=0, vmax=0.3) +ax[0].quiver(nemo_data_t.longitude.values, nemo_data_t.latitude.values, + data_u.to_array().values[0, :, :], data_v.to_array().values[0, :, :], + color='w', transform=data_crs, angles='xy', regrid_shape=40) + +ax[1].pcolormesh(nemo_data_t.longitude.values, nemo_data_t.latitude.values, speed, transform=data_crs, vmin=0, vmax=0.3) +ax[1].quiver(nemo_data_t.longitude.values, nemo_data_t.latitude.values, + u_new, v_new, + color='w', transform=data_crs, angles='xy', regrid_shape=40) + +ax[2].pcolormesh(nemo_data_t.longitude.values, nemo_data_t.latitude.values, speed, transform=data_crs, vmin=0, vmax=0.3) +ax[2].quiver(nemo_data_t.longitude.values, nemo_data_t.latitude.values, + u_pol, v_pol, + color='w', transform=data_crs, angles='xy', regrid_shape=40) + +ax[3].pcolormesh(nemo_data_t.longitude.values, nemo_data_t.latitude.values, speed, transform=data_crs, vmin=0, vmax=0.3) +ax[3].streamplot(nemo_data_t.longitude.values, nemo_data_t.latitude.values, + u_pol, v_pol, transform=data_crs, linewidth=1, density=2, color='w', zorder=101) + +ax[0].set_title('1: No Correction') +ax[1].set_title('2: NEMO Grid Correction') +ax[2].set_title('3: Grid Correction and Polar Correction') +ax[3].set_title('As left but with streamlines') + +fig.colorbar(cs, cax=cax, orientation='horizontal') +cax.set_xlabel('U (m s$^{-1}$)') + +#fig.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) +#fig.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) + +plt.show() +# uncomment this line to save an output image +fig.savefig(fn_out) + +``` +{{< imgproc quiver_plot Fit "800x800" >}} +{{< /imgproc >}} + + + # AMM15 - 1.5km resolution Atlantic Margin Model ```python diff --git 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Date: Mon, 11 Dec 2023 18:24:26 +0000 Subject: [PATCH 10/10] add configuration gallery --- .../Notebooks/General/polar_plotting.md | 128 ++-- .../Examples/Notebooks/General/zarr_files.md | 714 +++++++++++++----- .../en/docs/Examples/configs_gallery/index.md | 172 +++++ .../Examples/configs_gallery/quiver_plot.png | Bin 0 -> 622980 bytes static/zarr_files/zarr_files_33_1.png | Bin 0 -> 50045 bytes 5 files changed, 780 insertions(+), 234 deletions(-) create mode 100644 content/en/docs/Examples/configs_gallery/quiver_plot.png create mode 100644 static/zarr_files/zarr_files_33_1.png diff --git a/content/en/docs/Examples/Notebooks/General/polar_plotting.md b/content/en/docs/Examples/Notebooks/General/polar_plotting.md index c901533da7..6bdbc4fc97 100644 --- a/content/en/docs/Examples/Notebooks/General/polar_plotting.md +++ b/content/en/docs/Examples/Notebooks/General/polar_plotting.md @@ -152,63 +152,64 @@ The plot shows three cases: 1:no correction, 2:the NEMO grid correction, and 3:t ```python -# # Subplot axes settings -# n_r = 2 # Number of subplot rows -# n_c = 2 # Number of subplot columns -# figsize = (10, 10) # Figure size -# subplot_padding = 0.5 # Amount of vertical and horizontal padding between plots -# fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure padding (left, top, right, bottom) +# Subplot axes settings +n_r = 2 # Number of subplot rows +n_c = 2 # Number of subplot columns +figsize = (10, 10) # Figure size +subplot_padding = 0.5 # Amount of vertical and horizontal padding between plots +fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure padding (left, top, right, bottom) -# # Labels and Titles -# fig_title = "Velocity Plot" # Whole figure title +# Labels and Titles +fig_title = "Velocity Plot" # Whole figure title -# # Create plot and flatten axis array -# fig, ax = plt.subplots(n_r, n_c, subplot_kw={"projection": mrc}, sharey=True, sharex=True, figsize=figsize) -# cax = fig.add_axes([0.3, 0.96, 0.4, 0.01]) +# Create plot and flatten axis array +fig, ax = plt.subplots(n_r, n_c, subplot_kw={"projection": mrc}, sharey=True, sharex=True, figsize=figsize) +cax = fig.add_axes([0.3, 0.96, 0.4, 0.01]) -# ax = ax.flatten() -# for rr in range(n_r * n_c): -# ax[rr].add_feature(cfeature.LAND, zorder=100) -# ax[rr].gridlines() -# ax[rr].set_extent([-180, 180, 70, 90], crs=data_crs) -# coast._utils.plot_util.set_circle(ax[rr]) +ax = ax.flatten() +for rr in range(n_r * n_c): + ax[rr].add_feature(cfeature.LAND, zorder=100) + ax[rr].gridlines() + ax[rr].set_extent([-180, 180, 70, 90], crs=data_crs) + coast._utils.plot_util.set_circle(ax[rr]) -# cs = ax[0].pcolormesh(nemo_data_t.longitude.values, nemo_data_t.latitude.values, speed, transform=data_crs, vmin=0, vmax=0.3) -# ax[0].quiver(nemo_data_t.longitude.values, nemo_data_t.latitude.values, -# data_u.to_array().values[0, :, :], data_v.to_array().values[0, :, :], -# color='w', transform=data_crs, angles='xy', regrid_shape=40) +cs = ax[0].pcolormesh(nemo_data_t.longitude.values, nemo_data_t.latitude.values, speed, transform=data_crs, vmin=0, vmax=0.3) +ax[0].quiver(nemo_data_t.longitude.values, nemo_data_t.latitude.values, + data_u.to_array().values[0, :, :], data_v.to_array().values[0, :, :], + color='w', transform=data_crs, angles='xy', regrid_shape=40) -# ax[1].pcolormesh(nemo_data_t.longitude.values, nemo_data_t.latitude.values, speed, transform=data_crs, vmin=0, vmax=0.3) -# ax[1].quiver(nemo_data_t.longitude.values, nemo_data_t.latitude.values, -# u_new, v_new, -# color='w', transform=data_crs, angles='xy', regrid_shape=40) +ax[1].pcolormesh(nemo_data_t.longitude.values, nemo_data_t.latitude.values, speed, transform=data_crs, vmin=0, vmax=0.3) +ax[1].quiver(nemo_data_t.longitude.values, nemo_data_t.latitude.values, + u_new, v_new, + color='w', transform=data_crs, angles='xy', regrid_shape=40) -# ax[2].pcolormesh(nemo_data_t.longitude.values, nemo_data_t.latitude.values, speed, transform=data_crs, vmin=0, vmax=0.3) -# ax[2].quiver(nemo_data_t.longitude.values, nemo_data_t.latitude.values, -# u_pol, v_pol, -# color='w', transform=data_crs, angles='xy', regrid_shape=40) +ax[2].pcolormesh(nemo_data_t.longitude.values, nemo_data_t.latitude.values, speed, transform=data_crs, vmin=0, vmax=0.3) +ax[2].quiver(nemo_data_t.longitude.values, nemo_data_t.latitude.values, + u_pol, v_pol, + color='w', transform=data_crs, angles='xy', regrid_shape=40) -# ax[3].pcolormesh(nemo_data_t.longitude.values, nemo_data_t.latitude.values, speed, transform=data_crs, vmin=0, vmax=0.3) -# ax[3].streamplot(nemo_data_t.longitude.values, nemo_data_t.latitude.values, -# u_pol, v_pol, transform=data_crs, linewidth=1, density=2, color='w', zorder=101) +ax[3].pcolormesh(nemo_data_t.longitude.values, nemo_data_t.latitude.values, speed, transform=data_crs, vmin=0, vmax=0.3) +ax[3].streamplot(nemo_data_t.longitude.values, nemo_data_t.latitude.values, + u_pol, v_pol, transform=data_crs, linewidth=1, density=2, color='w', zorder=101) -# ax[0].set_title('1: No Correction') -# ax[1].set_title('2: NEMO Grid Correction') -# ax[2].set_title('3: Grid Correction and Polar Correction') -# ax[3].set_title('As left but with streamlines') +ax[0].set_title('1: No Correction') +ax[1].set_title('2: NEMO Grid Correction') +ax[2].set_title('3: Grid Correction and Polar Correction') +ax[3].set_title('As left but with streamlines') -# fig.colorbar(cs, cax=cax, orientation='horizontal') -# cax.set_xlabel('U (m s$^{-1}$)') +fig.colorbar(cs, cax=cax, orientation='horizontal') +cax.set_xlabel('U (m s$^{-1}$)') -# #fig.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) -# #fig.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) +#fig.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) +#fig.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) -# plt.show() -# # uncomment this line to save an output image +plt.show() +# uncomment this line to save an output image # fig.savefig(fn_out) ``` +![png](/COAsT/polar_plotting_files/polar_plotting_14_1.png) Below shows the temperature plots @@ -223,29 +224,30 @@ data_temp = nemo_data_t.temperature.isel(t_dim=0, z_dim=0) ```python -# figsize = (5, 5) # Figure size -# fig = plt.figure(figsize=figsize) -# ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.75], projection=mrc) -# cax = fig.add_axes([0.3, 0.96, 0.4, 0.01]) - -# ax1.add_feature(cfeature.LAND, zorder=105) -# ax1.gridlines() -# ax1.set_extent([-180, 180, 70, 90], crs=data_crs) -# coast._utils.plot_util.set_circle(ax1) - -# # We use to a function to re-project the data for plotting contours over the pole. -# cs1 = coast._utils.plot_util.plot_polar_contour( -# nemo_data_t.longitude.values, nemo_data_t.latitude.values, data_bathy, ax1, levels=6, colors="k", zorder=101 -# ) -# cs2 = ax1.pcolormesh( -# nemo_data_t.longitude.values, nemo_data_t.latitude.values, data_temp, transform=data_crs, vmin=-2, vmax=8 -# ) -# cax.set_xlabel(r"SST ($^{\circ}$C)") -# fig.colorbar(cs2, cax=cax, orientation="horizontal") - -# # fig.savefig(fn_out, dpi=120) +figsize = (5, 5) # Figure size +fig = plt.figure(figsize=figsize) +ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.75], projection=mrc) +cax = fig.add_axes([0.3, 0.96, 0.4, 0.01]) + +ax1.add_feature(cfeature.LAND, zorder=105) +ax1.gridlines() +ax1.set_extent([-180, 180, 70, 90], crs=data_crs) +coast._utils.plot_util.set_circle(ax1) + +# We use to a function to re-project the data for plotting contours over the pole. +cs1 = coast._utils.plot_util.plot_polar_contour( + nemo_data_t.longitude.values, nemo_data_t.latitude.values, data_bathy, ax1, levels=6, colors="k", zorder=101 +) +cs2 = ax1.pcolormesh( + nemo_data_t.longitude.values, nemo_data_t.latitude.values, data_temp, transform=data_crs, vmin=-2, vmax=8 +) +cax.set_xlabel(r"SST ($^{\circ}$C)") +fig.colorbar(cs2, cax=cax, orientation="horizontal") + +# fig.savefig(fn_out, dpi=120) ``` +![png](/COAsT/polar_plotting_files/polar_plotting_17_3.png) ```python diff --git a/content/en/docs/Examples/Notebooks/General/zarr_files.md b/content/en/docs/Examples/Notebooks/General/zarr_files.md index 5217b7af0e..a5bca5c3cd 100644 --- a/content/en/docs/Examples/Notebooks/General/zarr_files.md +++ b/content/en/docs/Examples/Notebooks/General/zarr_files.md @@ -6,6 +6,7 @@ description: > Zarr files example. --- + # Load Zarr files This tutorial will show some examples on how to load zarr files on coast. @@ -51,39 +52,8 @@ fn_nemo_dat_v = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/n06_V.za ``` - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pydap/lib.py:5: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap')`. - Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap.responses')`. - Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2350: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap')`. - Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap.handlers')`. - Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2350: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap')`. - Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap.tests')`. - Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2350: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('pydap')`. - Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pkg_resources/__init__.py:2871: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('sphinxcontrib')`. - Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages - - - - --------------------------------------------------------------------------- - - ModuleNotFoundError Traceback (most recent call last) - - Cell In[1], line 6 - 4 import numpy as np - 5 import xarray as xr - ----> 6 import zarr - 8 root = "./" - 9 fn_config_t_grid = root + "./config/example_nemo_monthly_climate.json" - - - ModuleNotFoundError: No module named 'zarr' + /mnt/code/.pyenv/versions/3.10.12/envs/coast-10/lib/python3.10/site-packages/utide/harmonics.py:16: RuntimeWarning: invalid value encountered in cast + /mnt/code/.pyenv/versions/3.10.12/envs/coast-10/lib/python3.10/site-packages/utide/harmonics.py:17: RuntimeWarning: invalid value encountered in cast ### Open the zarr files as a XARRAY @@ -97,18 +67,10 @@ mesh_zgr = xr.open_zarr(fn_nemo_dom_mesh_zgr) mesh_hgr = xr.open_zarr(fn_nemo_dom_mesh_hgr) ``` - - --------------------------------------------------------------------------- - - NameError Traceback (most recent call last) - - Cell In[2], line 1 - ----> 1 dom = xr.open_zarr(fn_nemo_dom_mask) - 2 mesh_zgr = xr.open_zarr(fn_nemo_dom_mesh_zgr) - 3 mesh_hgr = xr.open_zarr(fn_nemo_dom_mesh_hgr) - - - NameError: name 'fn_nemo_dom_mask' is not defined + /tmp/ipykernel_262236/396929176.py:1: RuntimeWarning: Failed to open Zarr store with consolidated metadata, but successfully read with non-consolidated metadata. This is typically much slower for opening a dataset. To silence this warning, consider: + 1. Consolidating metadata in this existing store with zarr.consolidate_metadata(). + 2. Explicitly setting consolidated=False, to avoid trying to read consolidate metadata, or + 3. Explicitly setting consolidated=True, to raise an error in this case instead of falling back to try reading non-consolidated metadata. @@ -120,20 +82,6 @@ for var_name in mesh_hgr.data_vars: ``` - --------------------------------------------------------------------------- - - NameError Traceback (most recent call last) - - Cell In[3], line 1 - ----> 1 for var_name in mesh_zgr.data_vars: - 2 dom[var_name] = mesh_zgr[var_name] - 3 for var_name in mesh_hgr.data_vars: - - - NameError: name 'mesh_zgr' is not defined - - - ```python u_grid = xr.open_zarr(fn_nemo_dat_u) u_grid = u_grid.isel(time_counter=slice(0,119)).rename({'depthu': 'depth'}) @@ -144,20 +92,6 @@ t_grid = t_grid.rename({'deptht': 'depth'}) ``` - --------------------------------------------------------------------------- - - NameError Traceback (most recent call last) - - Cell In[4], line 1 - ----> 1 u_grid = xr.open_zarr(fn_nemo_dat_u) - 2 u_grid = u_grid.isel(time_counter=slice(0,119)).rename({'depthu': 'depth'}) - 3 v_grid = xr.open_zarr(fn_nemo_dat_v) - - - NameError: name 'fn_nemo_dat_u' is not defined - - - ```python for var_name in u_grid.data_vars: t_grid[var_name] = u_grid[var_name] @@ -165,54 +99,22 @@ for var_name in v_grid.data_vars: t_grid[var_name] = v_grid[var_name] ``` - - --------------------------------------------------------------------------- - - NameError Traceback (most recent call last) - - Cell In[5], line 1 - ----> 1 for var_name in u_grid.data_vars: - 2 t_grid[var_name] = u_grid[var_name] - 3 for var_name in v_grid.data_vars: - - - NameError: name 'u_grid' is not defined + /mnt/code/.pyenv/versions/3.10.12/envs/coast-10/lib/python3.10/site-packages/dask/array/core.py:4836: PerformanceWarning: Increasing number of chunks by factor of 15 + /mnt/code/.pyenv/versions/3.10.12/envs/coast-10/lib/python3.10/site-packages/dask/array/core.py:4836: PerformanceWarning: Increasing number of chunks by factor of 15 ```python -dom +#Uncomment to see the data +# dom ``` - --------------------------------------------------------------------------- - - NameError Traceback (most recent call last) - - Cell In[6], line 1 - ----> 1 dom - - - NameError: name 'dom' is not defined - - - ```python -t_grid +#Uncomment to see the data +# t_grid ``` - - --------------------------------------------------------------------------- - - NameError Traceback (most recent call last) - - Cell In[7], line 1 - ----> 1 t_grid - - - NameError: name 't_grid' is not defined - - ### Slice the zarr files Because zarr files are optimized for cloud, when we instantiate an xarray dataset, we do not open the zarr by it self. We only open some metadata related to the file. The files will only be downloaed when we need to perform some processing on the data @@ -223,34 +125,10 @@ dom = dom.isel(y=slice(500, 700), x=slice(1000,1200)) ``` - --------------------------------------------------------------------------- - - NameError Traceback (most recent call last) - - Cell In[8], line 1 - ----> 1 dom = dom.isel(y=slice(500, 700), x=slice(1000,1200)) - - - NameError: name 'dom' is not defined - - - ```python t_grid = t_grid.isel(y=slice(500, 700), x=slice(1000,1200), time_counter=slice(0,24)) ``` - - --------------------------------------------------------------------------- - - NameError Traceback (most recent call last) - - Cell In[9], line 1 - ----> 1 t_grid = t_grid.isel(y=slice(500, 700), x=slice(1000,1200), time_counter=slice(0,24)) - - - NameError: name 't_grid' is not defined - - ### Loading and Interrogating We can create a new Gridded object by simple calling `coast.Gridded()`. By passing this a NEMO data file and a NEMO domain file, COAsT will combine the two into a single xarray dataset within the Gridded object. Each individual Gridded object should be for a specified NEMO grid type, which is specified in a configuration file which is also passed as an argument. The Dask library is switched on by default, chunking can be specified in the configuration file. @@ -260,16 +138,26 @@ We can create a new Gridded object by simple calling `coast.Gridded()`. By passi nemo_t = coast.Gridded(fn_data= t_grid, fn_domain = dom, config=fn_config_t_grid) ``` - - --------------------------------------------------------------------------- - - NameError Traceback (most recent call last) - - Cell In[10], line 1 - ----> 1 nemo_t = coast.Gridded(fn_data= t_grid, fn_domain = dom, config=fn_config_t_grid) - - - NameError: name 't_grid' is not defined + /mnt/code/code/noc/coast/COAsT/coast/data/gridded.py:236: UserWarning: The model domain loaded, ' + Dimensions: (t: 1, z: 75, y: 200, x: 200) + Dimensions without coordinates: t, z, y, x + Data variables: (12/34) + e3t_0 (t, z, y, x) float64 dask.array + e3t_1d (t, z) float64 dask.array + e3u_0 (t, z, y, x) float64 dask.array + e3v_0 (t, z, y, x) float64 dask.array + e3w_0 (t, z, y, x) float64 dask.array + e3w_1d (t, z) float64 dask.array + ... ... + glamu (t, y, x) float32 dask.array + glamv (t, y, x) float32 dask.array + gphif (t, y, x) float32 dask.array + gphit (t, y, x) float32 dask.array + gphiu (t, y, x) float32 dask.array + gphiv (t, y, x) float32 dask.array + Attributes: + DOMAIN_number_total: 8972 + DOMAIN_size_global: [4322, 3059]', does not contain the bathy_metry' variable. This will result in the NEMO.dataset.bathymetry variable being set to zero, which may result in unexpected behaviour from routines that require this variable. `!IMPORTANT!`: this dataset does not contain bathymetry data. @@ -293,17 +181,513 @@ ssh.compute() ``` - --------------------------------------------------------------------------- - - NameError Traceback (most recent call last) - Cell In[12], line 1 - ----> 1 ssh = nemo_t.dataset.ssh - 2 ssh.compute() - 3 # ssh # uncomment to print data object summary +

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.../Notebooks/Altimetry/altimetry_tutorial.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md b/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md index 657c6b12b5..50520ba4f3 100644 --- a/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md @@ -59,14 +59,14 @@ nemo = coast.Gridded(fn_nemo_dat, fn_nemo_dom, config=fn_nemo_config) altimetry = coast.Altimetry(fn_altimetry, config=fn_altimetry_config) ``` - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:278: UserWarning: The specified chunks separate the stored chunks along dimension "time_counter" starting at index 2. This could degrade performance. Instead, consider rechunking after loading. + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:282: UserWarning: The specified chunks separate the stored chunks along dimension "time_counter" starting at index 2. This could degrade performance. Instead, consider rechunking after loading. ././config/example_altimetry.json - Altimetry object at 0x560d5d3e2980 initialised + Altimetry object at 0x562b801a3980 initialised - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:278: UserWarning: The specified chunks separate the stored chunks along dimension "time" starting at index 1000. This could degrade performance. Instead, consider rechunking after loading. + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:282: UserWarning: The specified chunks separate the stored chunks along dimension "time" starting at index 1000. This could degrade performance. Instead, consider rechunking after loading. ### Subsetting @@ -82,7 +82,7 @@ ind = ind[::4] altimetry = altimetry.isel(t_dim=ind) ``` - Subsetting Altimetry object at 0x560d5d3e2980 indices in [-10, 10], [45, 60] + Subsetting Altimetry object at 0x562b801a3980 indices in [-10, 10], [45, 60] ### Model interpolation @@ -99,7 +99,7 @@ altimetry.obs_operator(nemo, mod_var_name="ssh", time_interp="nearest") # to see for yourself. ``` - Interpolating Gridded object at 0x560d5d3e2980 "ssh" with time_interp "nearest" + Interpolating Gridded object at 0x562b801a3980 "ssh" with time_interp "nearest" @@ -118,7 +118,7 @@ altimetry.obs_operator(nemo, mod_var_name="ssh", time_interp="nearest") stats = altimetry.basic_stats("ocean_tide_standard_name", "interp_ssh") ``` - Altimetry object at 0x560d5d3e2980 initialised + Altimetry object at 0x562b801a3980 initialised @@ -146,7 +146,7 @@ crps = altimetry.crps(nemo, model_var_name="ssh", obs_var_name="ocean_tide_stand #crps.dataset # uncomment to print data object summary ``` - Altimetry object at 0x560d5d3e2980 initialised + Altimetry object at 0x562b801a3980 initialised ### Plotting data From 472ebb526c593b52b09ad980aba57dc6ecab577c Mon Sep 17 00:00:00 2001 From: MarkDownBot Date: Mon, 11 Dec 2023 15:35:24 +0000 Subject: [PATCH 06/10] Updated Profile notebook pages from coast repo. --- .../Notebooks/Profile/introduction_to_profile_class.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/en/docs/Examples/Notebooks/Profile/introduction_to_profile_class.md b/content/en/docs/Examples/Notebooks/Profile/introduction_to_profile_class.md index d798828756..6933115dd1 100644 --- a/content/en/docs/Examples/Notebooks/Profile/introduction_to_profile_class.md +++ b/content/en/docs/Examples/Notebooks/Profile/introduction_to_profile_class.md @@ -336,7 +336,7 @@ differences.dataset.diff_temperature.plot() - + @@ -364,7 +364,7 @@ plt.colorbar( label='temperature diff (obs-model)') - + @@ -567,7 +567,7 @@ plt.legend() - + @@ -607,7 +607,7 @@ plt.colorbar() - + @@ -648,7 +648,7 @@ plt.colorbar() - + From b62c6038765e7e4be6673fa5c81b65e889ed1967 Mon Sep 17 00:00:00 2001 From: MarkDownBot Date: Mon, 11 Dec 2023 15:35:41 +0000 Subject: [PATCH 07/10] Updated Altimetry notebook pages from coast repo. --- .../tidegauge_validation_tutorial.md | 156 +++++++++--------- .../Notebooks/Tidegauge/tidetable_tutorial.md | 4 +- 2 files changed, 80 insertions(+), 80 deletions(-) diff --git a/content/en/docs/Examples/Notebooks/Tidegauge/tidegauge_validation_tutorial.md b/content/en/docs/Examples/Notebooks/Tidegauge/tidegauge_validation_tutorial.md index 9a562074ed..bd95a7a11c 100644 --- a/content/en/docs/Examples/Notebooks/Tidegauge/tidegauge_validation_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Tidegauge/tidegauge_validation_tutorial.md @@ -84,7 +84,7 @@ We can create our empty tidegauge object: tidegauge = coast.Tidegauge() ``` - Tidegauge object at 0x5627e1b35980 initialised + Tidegauge object at 0x55a7d8e51980 initialised The `Tidegauge` class contains multiple methods for reading different typical @@ -481,7 +481,7 @@ Coordinates: Dimensions without coordinates: id_dim, t_dim Data variables: ssh (id_dim, t_dim) float64 2.818 2.823 2.871 ... 3.214 3.257 3.371 - qc_flags (id_dim, t_dim) int64 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1 1