From 690f9a8378c7c883739e8b185e0e39f0cc40ca7c Mon Sep 17 00:00:00 2001 From: MarkDownBot Date: Tue, 31 Oct 2023 13:15:06 +0000 Subject: [PATCH 01/32] Updated Altimetry notebook pages from coast repo. --- .../Notebooks/Altimetry/altimetry_tutorial.md | 16 +++++----------- .../altimetry_tutorial_15_1.png | Bin 60952 -> 60952 bytes .../altimetry_tutorial_16_0.png | Bin 59188 -> 59188 bytes .../altimetry_tutorial_17_0.png | Bin 60713 -> 60713 bytes 4 files changed, 5 insertions(+), 11 deletions(-) diff --git a/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md b/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md index 93346ff41c2..43bbcb7b4cf 100644 --- a/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md @@ -41,7 +41,7 @@ altimetry = coast.Altimetry(fn_altimetry, config=fn_altimetry_config) ``` ././config/example_altimetry.json - Altimetry object at 0x561214f5efc0 initialised + Altimetry object at 0x561fb52d1fc0 initialised ### Subsetting @@ -57,7 +57,7 @@ ind = ind[::4] altimetry = altimetry.isel(t_dim=ind) ``` - Subsetting Altimetry object at 0x561214f5efc0 indices in [-10, 10], [45, 60] + Subsetting Altimetry object at 0x561fb52d1fc0 indices in [-10, 10], [45, 60] ### Model interpolation @@ -74,7 +74,7 @@ altimetry.obs_operator(nemo, mod_var_name="ssh", time_interp="nearest") # to see for yourself. ``` - Interpolating Gridded object at 0x561214f5efc0 "ssh" with time_interp "nearest" + Interpolating Gridded object at 0x561fb52d1fc0 "ssh" with time_interp "nearest" @@ -93,13 +93,7 @@ altimetry.obs_operator(nemo, mod_var_name="ssh", time_interp="nearest") stats = altimetry.basic_stats("ocean_tide_standard_name", "interp_ssh") ``` - Altimetry object at 0x561214f5efc0 initialised - - - /usr/share/miniconda/envs/coast/lib/python3.8/site-packages/coast/data/altimetry.py:351: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning. - corr = pdvar.corr(method=method) - /usr/share/miniconda/envs/coast/lib/python3.8/site-packages/coast/data/altimetry.py:365: FutureWarning: The default value of numeric_only in DataFrame.cov is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning. - cov = pdvar.cov() + Altimetry object at 0x561fb52d1fc0 initialised @@ -127,7 +121,7 @@ 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 0x561214f5efc0 initialised + Altimetry object at 0x561fb52d1fc0 initialised ### Plotting data diff --git a/static/altimetry_tutorial_files/altimetry_tutorial_15_1.png b/static/altimetry_tutorial_files/altimetry_tutorial_15_1.png index c51b43980ef8eed6ab34eff555cc8924afb9bc72..15de65558ac4d27b686543217902ee668c5455a0 100644 GIT binary patch delta 43 zcmbPnhk3>w<_R8h#yScaB_##LR{Hw6i6sR&`6W4-NqYH3>H6m!oL_8A+w>LyZu=4Y delta 43 zcmbPnhk3>w<_R8h2098EB_##LR{Hw6i6sR&`6W4-NqYH3>H0R}>jXBYZF&m;XD|@Z diff --git a/static/altimetry_tutorial_files/altimetry_tutorial_16_0.png b/static/altimetry_tutorial_files/altimetry_tutorial_16_0.png index ff9bfa755b920683ace4f09a850158c2a5eec572..7b387e1670f99689ace4c03410c74b1aacb75f2a 100644 GIT binary patch delta 43 zcmdmTj(N*D<_R8h#yScaB_##LR{Hw6i6sR&`6W4-NqYH3>H6m!oL_8AJNFC#bJ7wl delta 43 zcmdmTj(N*D<_R8h2098EB_##LR{Hw6i6sR&`6W4-NqYH3>H0R}>jXBYoqGlVYzGkm diff --git a/static/altimetry_tutorial_files/altimetry_tutorial_17_0.png b/static/altimetry_tutorial_files/altimetry_tutorial_17_0.png index 02b08bb65060e9bed00f29055aa5476acec8f12b..4a96ae7655785569fea0065f2f68eb1250455b43 100644 GIT binary patch delta 43 zcmZ2^i+SZO<_R8h#yScaB_##LR{Hw6i6sR&`6W4-NqYH3>H6m!oL_8AJNyO!bNv!2 delta 43 zcmZ2^i+SZO<_R8h2098EB_##LR{Hw6i6sR&`6W4-NqYH3>H0R}>jXBY9ex7 Date: Tue, 31 Oct 2023 13:16:38 +0000 Subject: [PATCH 02/32] Updated Profile notebook pages from coast repo. --- .../Profile/introduction_to_profile_class.md | 34 ++++++++++-------- .../Profile/wod_bgc_ragged_example.md | 6 ++++ .../introduction_to_profile_class_12_1.png | Bin 0 -> 55733 bytes .../introduction_to_profile_class_33_1.png | Bin 0 -> 27958 bytes .../introduction_to_profile_class_34_1.png | Bin 0 -> 23717 bytes .../introduction_to_profile_class_38_0.png | Bin 0 -> 56509 bytes .../introduction_to_profile_class_44_0.png | Bin 0 -> 54646 bytes .../introduction_to_profile_class_46_1.png | Bin 0 -> 34007 bytes .../introduction_to_profile_class_56_1.png | Bin 0 -> 20486 bytes .../introduction_to_profile_class_59_1.png | Bin 0 -> 17559 bytes .../introduction_to_profile_class_63_1.png | Bin 0 -> 18623 bytes .../wod_bgc_ragged_example_17_0.png | Bin 21785 -> 21785 bytes .../wod_bgc_ragged_example_17_1.png | Bin 23707 -> 23707 bytes 13 files changed, 26 insertions(+), 14 deletions(-) create mode 100644 static/introduction_to_profile_class_files/introduction_to_profile_class_12_1.png create mode 100644 static/introduction_to_profile_class_files/introduction_to_profile_class_33_1.png create mode 100644 static/introduction_to_profile_class_files/introduction_to_profile_class_34_1.png create mode 100644 static/introduction_to_profile_class_files/introduction_to_profile_class_38_0.png create mode 100644 static/introduction_to_profile_class_files/introduction_to_profile_class_44_0.png create mode 100644 static/introduction_to_profile_class_files/introduction_to_profile_class_46_1.png create mode 100644 static/introduction_to_profile_class_files/introduction_to_profile_class_56_1.png create mode 100644 static/introduction_to_profile_class_files/introduction_to_profile_class_59_1.png create mode 100644 static/introduction_to_profile_class_files/introduction_to_profile_class_63_1.png 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 0116c2bc964..c82ad28f2c3 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 @@ -145,7 +145,7 @@ profile.plot_map() -![png](/COAsT/0._profile_introduction_files/0._profile_introduction_12_1.png) +![png](/COAsT/introduction_to_profile_class_files/introduction_to_profile_class_12_1.png) @@ -200,6 +200,12 @@ nemo.dataset = nemo.dataset.rename({"depth_0": "depth"}) # profile methods will model_profiles = profile.obs_operator(nemo) ``` + /usr/share/miniconda/envs/coast/lib/python3.8/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. + mod_profiles["interp_lag"] = (["id_dim"], interp_lag) + /usr/share/miniconda/envs/coast/lib/python3.8/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. + mod_profiles["interp_lag"] = (["id_dim"], interp_lag) + + Now that we have interpolated the model onto Profiles, we have a new Profile object called `model_profiles`. This can be used to do some comparisons with our original `processed_profile` object, which we created above. @@ -305,13 +311,13 @@ differences.dataset.diff_temperature.plot() - + -![png](/COAsT/0._profile_introduction_files/0._profile_introduction_33_1.png) +![png](/COAsT/introduction_to_profile_class_files/introduction_to_profile_class_33_1.png) @@ -333,13 +339,13 @@ plt.colorbar( label='temperature diff (obs-model)') - + -![png](/COAsT/0._profile_introduction_files/0._profile_introduction_34_1.png) +![png](/COAsT/introduction_to_profile_class_files/introduction_to_profile_class_34_1.png) @@ -372,7 +378,7 @@ surface_errors.plot_map(var_str="diff_temperature") -![png](/COAsT/0._profile_introduction_files/0._profile_introduction_38_0.png) +![png](/COAsT/introduction_to_profile_class_files/introduction_to_profile_class_38_0.png) @@ -418,7 +424,7 @@ bottom_errors.plot_map(var_str="diff_temperature") -![png](/COAsT/0._profile_introduction_files/0._profile_introduction_44_0.png) +![png](/COAsT/introduction_to_profile_class_files/introduction_to_profile_class_44_0.png) @@ -455,7 +461,7 @@ plt.title("Temperature diff (obs-model)") -![png](/COAsT/0._profile_introduction_files/0._profile_introduction_46_1.png) +![png](/COAsT/introduction_to_profile_class_files/introduction_to_profile_class_46_1.png) @@ -533,13 +539,13 @@ plt.legend() - + -![png](/COAsT/0._profile_introduction_files/0._profile_introduction_56_1.png) +![png](/COAsT/introduction_to_profile_class_files/introduction_to_profile_class_56_1.png) @@ -573,13 +579,13 @@ plt.colorbar() - + -![png](/COAsT/0._profile_introduction_files/0._profile_introduction_59_1.png) +![png](/COAsT/introduction_to_profile_class_files/introduction_to_profile_class_59_1.png) @@ -614,12 +620,12 @@ plt.colorbar() - + -![png](/COAsT/0._profile_introduction_files/0._profile_introduction_63_1.png) +![png](/COAsT/introduction_to_profile_class_files/introduction_to_profile_class_63_1.png) 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 71c6cf6f337..ac0355a044e 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 @@ -109,6 +109,12 @@ model_profiles = wod_profile_sub.obs_operator(seasia_bgc) #model_profiles.dataset # uncomment to print data object summary ``` + /usr/share/miniconda/envs/coast/lib/python3.8/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. + mod_profiles["interp_lag"] = (["id_dim"], interp_lag) + /usr/share/miniconda/envs/coast/lib/python3.8/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. 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    • @@ -720,7 +720,7 @@ plt.legend() - + @@ -752,7 +752,7 @@ mask_mean_SST.plot() - + @@ -780,7 +780,7 @@ plt.legend() - + 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 a2237663efd..6069367fe64 100644 --- a/content/en/docs/Examples/Notebooks/General/seasonal_decomp_example.md +++ b/content/en/docs/Examples/Notebooks/General/seasonal_decomp_example.md @@ -71,6 +71,10 @@ temperature = xr.DataArray( ) ``` + /tmp/ipykernel_6237/2929135463.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. + temperature = xr.DataArray( + + Check out the new data @@ -86,7 +90,7 @@ temperature[0,0,:,:].plot() - + @@ -107,7 +111,7 @@ temperature[:,0,200,200].plot() - [] + [] @@ -525,7 +529,7 @@ 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
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      • @@ -551,7 +551,7 @@ tt = xr.open_dataset(fn_tg) obs = coast.Tidegauge(dataset=tt) ``` - Tidegauge object at 0x561eabdbafc0 initialised + Tidegauge object at 0x56032c641fc0 initialised @@ -562,7 +562,7 @@ obs = coast.Tidegauge(dataset=tt) obs.dataset = obs.dataset.set_coords("time") ``` - Tidegauge object at 0x561eabdbafc0 initialised + Tidegauge object at 0x56032c641fc0 initialised ## Quick plotting to visualise Tidegauge data @@ -631,7 +631,7 @@ obs.isel(id_dim=stn_id).plot_timeseries(date_start=np.datetime64('2007-01-01'), (
        , - ) + ) @@ -663,9 +663,9 @@ 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 0x561eabdbafc0 initialised - Tidegauge object at 0x561eabdbafc0 initialised - Tidegauge object at 0x561eabdbafc0 initialised + Tidegauge object at 0x56032c641fc0 initialised + Tidegauge object at 0x56032c641fc0 initialised + Tidegauge object at 0x56032c641fc0 initialised @@ -716,7 +716,7 @@ 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 0x561eabdbafc0 initialised + Tidegauge object at 0x56032c641fc0 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. @@ -729,7 +729,7 @@ end_date = datetime.datetime(2007, 1, 31) obs = obs.time_slice(start_date, end_date) ``` - Tidegauge object at 0x561eabdbafc0 initialised + Tidegauge object at 0x56032c641fc0 initialised ### Interpolate model data onto obs locations @@ -747,7 +747,7 @@ model_timeseries.dataset Indexing model data at tide gauge locations.. Calculating interpolation distances. Interpolating in time... - Tidegauge object at 0x561eabdbafc0 initialised + Tidegauge object at 0x56032c641fc0 initialised @@ -1143,7 +1143,7 @@ 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
      • @@ -1392,8 +1392,8 @@ tganalysis = coast.TidegaugeAnalysis() obs_new, model_new = tganalysis.match_missing_values(obs.dataset.ssh, model_timeseries.dataset.ssh) ``` - Tidegauge object at 0x561eabdbafc0 initialised - Tidegauge object at 0x561eabdbafc0 initialised + Tidegauge object at 0x56032c641fc0 initialised + Tidegauge object at 0x56032c641fc0 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. @@ -1419,8 +1419,8 @@ model_new = tganalysis.demean_timeseries(model_new.dataset) # easily compared. ``` - Tidegauge object at 0x561eabdbafc0 initialised - Tidegauge object at 0x561eabdbafc0 initialised + Tidegauge object at 0x56032c641fc0 initialised + Tidegauge object at 0x56032c641fc0 initialised @@ -1626,7 +1626,7 @@ tide_obs = tganalysis.reconstruct_tide_utide(obs_new.dataset.time, ha_obs) prep/calcs ... done. prep/calcs ... done. prep/calcs ... done. - Tidegauge object at 0x561eabdbafc0 initialised + Tidegauge object at 0x56032c641fc0 initialised prep/calcs ... done. prep/calcs ... done. prep/calcs ... done. @@ -1676,7 +1676,7 @@ tide_obs = tganalysis.reconstruct_tide_utide(obs_new.dataset.time, ha_obs) prep/calcs ... done. prep/calcs ... done. prep/calcs ... done. - Tidegauge object at 0x561eabdbafc0 initialised + Tidegauge object at 0x56032c641fc0 initialised Get new TidegaugeMultiple objects containing non tidal residuals: @@ -1690,8 +1690,8 @@ ntr_obs = tganalysis.calculate_non_tidal_residuals(obs_new.dataset.ssh, tide_obs ntr_obs.dataset ``` - Tidegauge object at 0x561eabdbafc0 initialised - Tidegauge object at 0x561eabdbafc0 initialised + Tidegauge object at 0x56032c641fc0 initialised + Tidegauge object at 0x56032c641fc0 initialised @@ -2069,7 +2069,7 @@ Coordinates: site_name (id_dim) object '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
        • @@ -2180,7 +2180,7 @@ dx0.dataset ``` - Tidegauge object at 0x561eabdbafc0 initialised + Tidegauge object at 0x56032c641fc0 initialised @@ -2558,7 +2558,7 @@ 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
          • @@ -3095,7 +3095,7 @@ 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
            • threshold
              PandasIndex
              PandasIndex(Index([                  -2.0,                   -1.9,    -1.7999999999999998,
              +          -1.6999999999999997,    -1.5999999999999996,    -1.4999999999999996,
              +          -1.3999999999999995,    -1.2999999999999994,    -1.1999999999999993,
              +          -1.0999999999999992,    -0.9999999999999991,     -0.899999999999999,
              +          -0.7999999999999989,    -0.6999999999999988,    -0.5999999999999988,
              +         -0.49999999999999867,    -0.3999999999999986,    -0.2999999999999985,
              +          -0.1999999999999984,   -0.09999999999999831, 1.7763568394002505e-15,
              +          0.10000000000000187,    0.20000000000000195,    0.30000000000000204,
              +          0.40000000000000213,     0.5000000000000022,     0.6000000000000023,
              +           0.7000000000000024,     0.8000000000000025,     0.9000000000000026,
              +           1.0000000000000027,     1.1000000000000028,     1.2000000000000028,
              +            1.300000000000003,      1.400000000000003,      1.500000000000003,
              +           1.6000000000000032,     1.7000000000000033,     1.8000000000000034,
              +           1.9000000000000035],
              +      dtype='float64', name='threshold'))
          • @@ -3271,7 +3265,7 @@ plt.plot(thresh_obs.threshold[22], normalised_event_count[22], 'r+', markersize= - [] + [] @@ -3297,8 +3291,8 @@ ssh_diff = tganalysis.difference(obs_new.dataset, model_new.dataset) ntr_diff.dataset ``` - Tidegauge object at 0x561eabdbafc0 initialised - Tidegauge object at 0x561eabdbafc0 initialised + Tidegauge object at 0x56032c641fc0 initialised + Tidegauge object at 0x56032c641fc0 initialised @@ -3678,10 +3672,10 @@ 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 8c023734a39..add63d91c25 100644 --- a/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md @@ -53,7 +53,7 @@ Initiate a TideGauge object, if a filename is passed it assumes it is a GESLA ty tg = coast.Tidegauge() ``` - Tidegauge object at 0x55d4e8c40fc0 initialised + Tidegauge object at 0x565340358fc0 initialised Specify the data read as a High Low Water dataset. @@ -63,6 +63,12 @@ Specify the data read as a High Low Water dataset. tg.read_hlw(filnam, date_start, date_end) ``` + /usr/share/miniconda/envs/coast/lib/python3.8/site-packages/coast/data/tidegauge.py:564: 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. + dataset = dataset.assign_coords(time=("time", time)) + /usr/share/miniconda/envs/coast/lib/python3.8/site-packages/coast/data/tidegauge.py:564: 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. + dataset = dataset.assign_coords(time=("time", time)) + + Show dataset. If timezone is specified then it is presented as requested, otherwise uses UTC. @@ -84,7 +90,7 @@ tg.dataset.plot.scatter(x="time", y="ssh") - + @@ -167,6 +173,10 @@ HLW = tg.get_tide_table_times(np.datetime64("2020-10-13 12:48"), winsize=2, meth HLW = tg.get_tide_table_times(np.datetime64("2020-10-13 12:48"), winsize=1, method="nearest_1") ``` + /usr/share/miniconda/envs/coast/lib/python3.8/site-packages/coast/data/tidegauge.py:665: 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. + return xr.DataArray([np.NaN], dims=(time_var), coords={time_var: [time_guess]})[0] + + ```python diff --git a/static/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_16_1.png b/static/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_16_1.png index f44a4d0f1df81d22403d0ca1cb8bcaa768e08bb1..506e6e6026c193886b2177326a051ab81a5fdc44 100644 GIT binary patch delta 43 zcmaELn)%gf<_R8h#yScaB_##LR{Hw6i6sR&`6W4-NqYH3>H6m!oL_8AYk3L)gUk{~ delta 43 zcmaELn)%gf<_R8h2098EB_##LR{Hw6i6sR&`6W4-NqYH3>H0R}>jXBYwLAp?d;t+0 diff --git a/static/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_18_0.png b/static/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_18_0.png index 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.../Gridded/introduction_to_gridded_class.md | 4 ++-- .../Gridded/potential_energy_tutorial.md | 20 +++++++++--------- .../Notebooks/Gridded/pycnocline_tutorial.md | 16 +++++++------- .../seasia_dic_example_plot_tutorial.md | 2 +- .../contour_tutorial_12_1.png | Bin 97393 -> 97393 bytes .../introduction_to_gridded_class_11_1.png | Bin 73195 -> 73195 bytes .../introduction_to_gridded_class_20_1.png | Bin 119133 -> 119133 bytes .../potential_energy_tutorial_11_1.png | Bin 0 -> 59458 bytes .../potential_energy_tutorial_21_1.png | Bin 5725 -> 5725 bytes .../pycnocline_tutorial_17_1.png | Bin 53300 -> 55213 bytes .../pycnocline_tutorial_17_2.png | Bin 0 -> 53300 bytes .../seasia_dic_example_plot_tutorial_8_1.png | Bin 119133 -> 119133 bytes .../transect_tutorial_10_0.png | Bin 25674 -> 25674 bytes .../transect_tutorial_20_0.png | Bin 32601 -> 32601 bytes .../transect_tutorial_22_0.png | Bin 23092 -> 23092 bytes 15 files changed, 21 insertions(+), 21 deletions(-) create mode 100644 static/potential_energy_tutorial_files/potential_energy_tutorial_11_1.png create mode 100644 static/pycnocline_tutorial_files/pycnocline_tutorial_17_2.png 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 50ddccdf94e..bb92ad7ff09 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 @@ -87,7 +87,7 @@ plt.pcolormesh(nemo_t.dataset.longitude, nemo_t.dataset.latitude, nemo_t.dataset - + @@ -161,7 +161,7 @@ plt.ylabel("latitude") plt.show() ``` - /tmp/ipykernel_3888/2498690501.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_6144/2498690501.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. plt.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 37feef8b4e5..6bfd89f4172 100644 --- a/content/en/docs/Examples/Notebooks/Gridded/potential_energy_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Gridded/potential_energy_tutorial.md @@ -75,17 +75,17 @@ strat.quick_plot('PEA') ``` - -![png](/COAsT/potential_energy_tutorial_files/potential_energy_tutorial_11_0.png) - + (
            , + ) - (
            , - ) + +![png](/COAsT/potential_energy_tutorial_files/potential_energy_tutorial_11_1.png) + @@ -467,10 +467,10 @@ 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 bd918bc0f40..00d77d3eaaf 100644 --- a/content/en/docs/Examples/Notebooks/Gridded/pycnocline_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Gridded/pycnocline_tutorial.md @@ -118,21 +118,21 @@ strat.quick_plot() ``` - -![png](/COAsT/pycnocline_tutorial_files/pycnocline_tutorial_17_0.png) - + (
              , + ) - -![png](/COAsT/pycnocline_tutorial_files/pycnocline_tutorial_17_1.png) - + +![png](/COAsT/pycnocline_tutorial_files/pycnocline_tutorial_17_1.png) + - (
              , - ) + +![png](/COAsT/pycnocline_tutorial_files/pycnocline_tutorial_17_2.png) + 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 79f28623487..3cb2b1688b6 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 @@ -56,7 +56,7 @@ plt.ylabel("latitude") plt.show() ``` - /tmp/ipykernel_3997/1161426776.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_6245/1161426776.py:2: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. 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              @@ -551,7 +551,7 @@ tt = xr.open_dataset(fn_tg) obs = coast.Tidegauge(dataset=tt) ``` - Tidegauge object at 0x56032c641fc0 initialised + Tidegauge object at 0x563dc355e980 initialised @@ -562,7 +562,7 @@ obs = coast.Tidegauge(dataset=tt) obs.dataset = obs.dataset.set_coords("time") ``` - Tidegauge object at 0x56032c641fc0 initialised + Tidegauge object at 0x563dc355e980 initialised ## Quick plotting to visualise Tidegauge data @@ -574,7 +574,7 @@ Tidegauge has ready made quick plotting routines for viewing time series and tid fig, ax = obs.plot_on_map() ``` - /usr/share/miniconda/envs/coast/lib/python3.8/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip warnings.warn(f'Downloading: {url}', DownloadWarning) @@ -631,7 +631,7 @@ obs.isel(id_dim=stn_id).plot_timeseries(date_start=np.datetime64('2007-01-01'), (
              , - ) + ) @@ -663,9 +663,9 @@ 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 0x56032c641fc0 initialised - Tidegauge object at 0x56032c641fc0 initialised - Tidegauge object at 0x56032c641fc0 initialised + Tidegauge object at 0x563dc355e980 initialised + Tidegauge object at 0x563dc355e980 initialised + Tidegauge object at 0x563dc355e980 initialised @@ -688,6 +688,10 @@ 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. + warnings.warn( + + interpolate model onto obs locations ``` tidegauge_from_model = obs.obs_operator(nemo, time_interp='nearest') @@ -716,7 +720,7 @@ 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 0x56032c641fc0 initialised + Tidegauge object at 0x563dc355e980 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. @@ -729,7 +733,7 @@ end_date = datetime.datetime(2007, 1, 31) obs = obs.time_slice(start_date, end_date) ``` - Tidegauge object at 0x56032c641fc0 initialised + Tidegauge object at 0x563dc355e980 initialised ### Interpolate model data onto obs locations @@ -747,7 +751,7 @@ model_timeseries.dataset Indexing model data at tide gauge locations.. Calculating interpolation distances. Interpolating in time... - Tidegauge object at 0x56032c641fc0 initialised + Tidegauge object at 0x563dc355e980 initialised @@ -1143,7 +1147,7 @@ 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
            • @@ -1392,8 +1396,8 @@ tganalysis = coast.TidegaugeAnalysis() obs_new, model_new = tganalysis.match_missing_values(obs.dataset.ssh, model_timeseries.dataset.ssh) ``` - Tidegauge object at 0x56032c641fc0 initialised - Tidegauge object at 0x56032c641fc0 initialised + Tidegauge object at 0x563dc355e980 initialised + Tidegauge object at 0x563dc355e980 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. @@ -1419,8 +1423,8 @@ model_new = tganalysis.demean_timeseries(model_new.dataset) # easily compared. ``` - Tidegauge object at 0x56032c641fc0 initialised - Tidegauge object at 0x56032c641fc0 initialised + Tidegauge object at 0x563dc355e980 initialised + Tidegauge object at 0x563dc355e980 initialised @@ -1626,57 +1630,67 @@ tide_obs = tganalysis.reconstruct_tide_utide(obs_new.dataset.time, ha_obs) prep/calcs ... done. prep/calcs ... done. prep/calcs ... done. - Tidegauge object at 0x56032c641fc0 initialised - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - Tidegauge object at 0x56032c641fc0 initialised + + + + --------------------------------------------------------------------------- + + KeyError Traceback (most recent call last) + + Cell In[23], line 1 + ----> 1 tide_mod = tganalysis.reconstruct_tide_utide(model_new.dataset.time, ha_mod) + 2 tide_obs = tganalysis.reconstruct_tide_utide(obs_new.dataset.time, ha_obs) + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/coast/diagnostics/tidegauge_analysis.py:163, in TidegaugeAnalysis.reconstruct_tide_utide(cls, data_array, utide_solution_list, constit, output_name) + 161 ds_out = xr.Dataset(data_array.coords) + 162 ds_out[output_name] = (["id_dim", "t_dim"], reconstructed) + --> 163 ds_out = ds_out.assign_coords(time=("t_dim", time.data)) + 165 return Tidegauge(dataset=ds_out) + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/common.py:621, in DataWithCoords.assign_coords(self, coords, **coords_kwargs) + 618 else: + 619 results = self._calc_assign_results(coords_combined) + --> 621 data.coords.update(results) + 622 return data + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/coordinates.py:552, in Coordinates.update(self, other) + 542 other_coords = create_coords_with_default_indexes( + 543 getattr(other, "variables", other) + 544 ) + 546 # Discard original indexed coordinates prior to merge allows to: + 547 # - fail early if the new coordinates don't preserve the integrity of existing + 548 # multi-coordinate indexes + 549 # - drop & replace coordinates without alignment (note: we must keep indexed + 550 # coordinates extracted from the DataArray objects passed as values to + 551 # `other` - if any - as those are still used for aligning the old/new coordinates) + --> 552 coords_to_align = drop_indexed_coords(set(other_coords) & set(other), self) + 554 coords, indexes = merge_coords( + 555 [coords_to_align, other_coords], + 556 priority_arg=1, + 557 indexes=coords_to_align.xindexes, + 558 ) + 560 # special case for PandasMultiIndex: updating only its dimension coordinate + 561 # is still allowed but depreciated. + 562 # It is the only case where we need to actually drop coordinates here (multi-index levels) + 563 # TODO: remove when removing PandasMultiIndex's dimension coordinate. + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/coordinates.py:918, in drop_indexed_coords(coords_to_drop, coords) + 911 raise ValueError( + 912 f"cannot drop or update coordinate(s) {idx_drop_coords_str}, which would corrupt " + 913 f"the following index built from coordinates {idx_coords_str}:\n" + 914 f"{idx}" + 915 ) + 917 for k in idx_drop_coords: + --> 918 del new_variables[k] + 919 del new_indexes[k] + 921 return Coordinates._construct_direct(coords=new_variables, indexes=new_indexes) + + + KeyError: 'time' Get new TidegaugeMultiple objects containing non tidal residuals: @@ -1690,8 +1704,37 @@ ntr_obs = tganalysis.calculate_non_tidal_residuals(obs_new.dataset.ssh, tide_obs ntr_obs.dataset ``` - Tidegauge object at 0x56032c641fc0 initialised - Tidegauge object at 0x56032c641fc0 initialised + + --------------------------------------------------------------------------- + + NameError Traceback (most recent call last) + + Cell In[24], line 1 + ----> 1 ntr_mod = tganalysis.calculate_non_tidal_residuals(model_new.dataset.ssh, tide_mod.dataset.reconstructed, apply_filter=False) + 2 ntr_obs = tganalysis.calculate_non_tidal_residuals(obs_new.dataset.ssh, tide_obs.dataset.reconstructed, apply_filter=True, window_length=10, polyorder=2) + 4 # Take a look + + + NameError: name 'tide_mod' is not defined + + +The dataset structure is preserved and has created a new variable called `ntr` - non-tidal residual. + +By default, this routines will apply `scipy.signal.savgol_filter` to the non-tidal residuals +to remove some noise. This can be switched off using `apply_filter = False`. + +The Doodson X0 filter is an alternative way of estimating non-tidal residuals. This will return a new Tidegauge() object containing filtered ssh data: + + +```python +dx0 = tganalysis.doodson_x0_filter(obs_new.dataset, "ssh") + +# take a look +dx0.dataset + +``` + + Tidegauge object at 0x563dc355e980 initialised @@ -2063,13 +2106,52 @@ dl.xr-attrs {
              <xarray.Dataset>
               Dimensions:    (t_dim: 720, id_dim: 61)
               Coordinates:
              -    time       (t_dim) datetime64[ns] 2007-01-01 ... 2007-01-30T23:00:00
                   longitude  (id_dim) float64 1.292 -3.975 -5.158 -5.051 ... 7.567 350.8 8.717
                   latitude   (id_dim) float64 51.95 51.57 57.9 51.71 ... 58.0 51.53 53.87
                   site_name  (id_dim) object '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
              • -The dataset structure is preserved and has created a new variable called `ntr` - non-tidal residual. -By default, this routines will apply `scipy.signal.savgol_filter` to the non-tidal residuals -to remove some noise. This can be switched off using `apply_filter = False`. -The Doodson X0 filter is an alternative way of estimating non-tidal residuals. This will return a new Tidegauge() object containing filtered ssh data: +```python +``` -```python -dx0 = tganalysis.doodson_x0_filter(obs_new.dataset, "ssh") +We can compare these analyses e.g. the observed timeseries against the harmonic reconstruction. Noting we can in principle extend the harmoninc reconstruction beyond the observation time window. -# take a look -dx0.dataset +```python +plt.figure() +plt.plot( tide_obs.dataset.time, tide_obs.dataset.reconstructed.isel(id_dim=stn_id), label='reconstructed from harmonics', color='orange' ) +plt.plot( obs_new.dataset.time, obs_new.dataset.ssh.isel(id_dim=stn_id) , label='observed', color='blue' ) +plt.legend() +plt.xticks(rotation=45) +plt.show() ``` - Tidegauge object at 0x56032c641fc0 initialised + --------------------------------------------------------------------------- + NameError Traceback (most recent call last) + Cell In[26], line 2 + 1 plt.figure() + ----> 2 plt.plot( tide_obs.dataset.time, tide_obs.dataset.reconstructed.isel(id_dim=stn_id), label='reconstructed from harmonics', color='orange' ) + 3 plt.plot( obs_new.dataset.time, obs_new.dataset.ssh.isel(id_dim=stn_id) , label='observed', color='blue' ) + 4 plt.legend() -
                - - - - - - - - - - - - - - -
                <xarray.Dataset>
                -Dimensions:    (t_dim: 720, id_dim: 61)
                -Coordinates:
                -    latitude   (id_dim) float64 51.95 51.57 57.9 51.71 ... 58.0 51.53 53.87
                -    site_name  (id_dim) object 'Harwich' 'Mumbles' 'Ullapool' ... 'N/A' 'N/A'
                -    longitude  (id_dim) float64 1.292 -3.975 -5.158 -5.051 ... 7.567 350.8 8.717
                -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
                +```python +normalised_event_count = 100 * thresh_obs.time_over_threshold_ntr.isel(id_dim=stn_id) / thresh_obs.time_over_threshold_ntr.isel(id_dim=stn_id).max() +plt.plot( thresh_obs.threshold, normalised_event_count ) +plt.ylim([20,40]) +plt.xlim([0.0,0.4]) +plt.title(f"time over threshold") +plt.xlabel('Analysis threshold (m)') +plt.ylabel('normalised event count (%)') +plt.plot(thresh_obs.threshold[22], normalised_event_count[22], 'r+', markersize=20) +``` + --------------------------------------------------------------------------- -```python + NameError Traceback (most recent call last) -``` + Cell In[31], line 1 + ----> 1 normalised_event_count = 100 * thresh_obs.time_over_threshold_ntr.isel(id_dim=stn_id) / thresh_obs.time_over_threshold_ntr.isel(id_dim=stn_id).max() + 3 plt.plot( thresh_obs.threshold, normalised_event_count ) + 4 plt.ylim([20,40]) -We can compare these analyses e.g. the observed timeseries against the harmonic reconstruction. Noting we can in principle extend the harmoninc reconstruction beyond the observation time window. + NameError: name 'thresh_obs' is not defined -```python -plt.figure() -plt.plot( tide_obs.dataset.time, tide_obs.dataset.reconstructed.isel(id_dim=stn_id), label='reconstructed from harmonics', color='orange' ) -plt.plot( obs_new.dataset.time, obs_new.dataset.ssh.isel(id_dim=stn_id) , label='observed', color='blue' ) -plt.legend() -plt.xticks(rotation=45) -plt.show() -``` - -![png](/COAsT/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_57_0.png) - +## Other TidegaugeAnalysis methods +### Calculate errors -We can also look closer at the difference between the observed timeseries and the harmonic reconstruction, that is the non-tidal residual. And we can compare the observed and modelled non-harmonic residual and contrast the different methods of removing the tides. Here we contrast using a harmonic analysis (and creating a "non-tidal residual") with the DoodsonX0 filter. Note that the timeseries is far too short for a sensible analysis and that this is really a demonstration of concept. +The difference() routine will calculate differences, absolute_differences and squared differenced for all variables. Corresponding new variables are created with names `diff_*`, `abs_diff_*` and `square_diff_*` ```python -plt.figure() -plt.subplot(2,1,1) -plt.plot( dx0.dataset.time, dx0.isel(id_dim=stn_id).dataset.ssh, label='obs: doodsonX0 filtered', color='orange' ) -plt.plot( ntr_obs.dataset.time, ntr_obs.isel(id_dim=stn_id).dataset.ntr, label='obs: non-tidal residual', color='blue' ) -plt.title('analysis comparison: non-tidal residual vs doodsonX0') -plt.legend() -plt.xticks(rotation=45) - -plt.subplot(2,1,2) -plt.plot( ntr_mod.dataset.time, ntr_mod.isel(id_dim=stn_id).dataset.ntr, label='model: non-tidal residual', color='g' ) -plt.plot( ntr_obs.dataset.time, ntr_obs.isel(id_dim=stn_id).dataset.ntr, label='obs: non-tidal residual', color='blue' ) -plt.title('model vs observation: non-tidal residual') -plt.legend() -plt.xticks(rotation=45) - -plt.tight_layout() +ntr_diff = tganalysis.difference(ntr_obs.dataset, ntr_mod.dataset) +ssh_diff = tganalysis.difference(obs_new.dataset, model_new.dataset) +# Take a look +ntr_diff.dataset ``` - -![png](/COAsT/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_59_0.png) - + --------------------------------------------------------------------------- + NameError Traceback (most recent call last) -### Threshold Statistics for non-tidal residuals + Cell In[32], line 1 + ----> 1 ntr_diff = tganalysis.difference(ntr_obs.dataset, ntr_mod.dataset) + 2 ssh_diff = tganalysis.difference(obs_new.dataset, model_new.dataset) + 4 # Take a look -This is a simple extreme value analysis of whatever data you use. It will count the number of peaks and the total time spent over each threshold provided. It will also count the numbers of daily and monthly maxima over each threshold. To this, a Tidegauge object and an array of thresholds (in metres) should be passed. The method return `peak_count_*`, `time_over_threshold_*`, `dailymax_count_*`, `monthlymax_count_*`: + NameError: name 'ntr_obs' is not defined -```python -thresh_mod = tganalysis.threshold_statistics(ntr_mod.dataset, thresholds=np.arange(-2, 2, 0.1)) -thresh_obs = tganalysis.threshold_statistics(ntr_obs.dataset, thresholds=np.arange(-2, 2, 0.1)) -``` +We can then easily get mean errors, MAE and MSE + ```python -# Have a look -thresh_obs +mean_stats = ntr_diff.dataset.mean(dim="t_dim", skipna=True) ``` + --------------------------------------------------------------------------- + NameError Traceback (most recent call last) -
                - - - - - - - - - - - - - - -
                <xarray.Dataset>
                -Dimensions:                  (t_dim: 720, id_dim: 61, threshold: 40)
                -Coordinates:
                -    latitude                 (id_dim) float64 51.95 51.57 57.9 ... 51.53 53.87
                -    site_name                (id_dim) object 'Harwich' 'Mumbles' ... 'N/A' 'N/A'
                -    longitude                (id_dim) float64 1.292 -3.975 ... 350.8 8.717
                -  * threshold                (threshold) float64 -2.0 -1.9 -1.8 ... 1.7 1.8 1.9
                -Dimensions without coordinates: t_dim, id_dim
                -Data variables:
                -    time                     (t_dim) datetime64[ns] 2007-01-01 ... 2007-01-30...
                -    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
                - - - - -```python -plt.plot( thresh_obs.threshold, thresh_obs.peak_count_ntr.mean(dim='id_dim') ) -#plt.ylim([0,15]) -plt.title(f"Observed peaks over threshold") -plt.xlabel('Analysis threshold (m)') -plt.ylabel('event count') -``` - - - - - Text(0, 0.5, 'event count') - - - - - -![png](/COAsT/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_63_1.png) - - - -Note that the non-tidal residual is a noisy timeseries (computed as a difference between two timeseries) so peaks do not necessarily correspond to peaks in total water level. For this reason `time_over_threshold_*` can be useful. Below we see that about 28% (y-axis) of the observations of non-tidal residual exceed 20cm (x-axis). Again, threshold statistics are more useful with a longer record. - - -```python -normalised_event_count = 100 * thresh_obs.time_over_threshold_ntr.isel(id_dim=stn_id) / thresh_obs.time_over_threshold_ntr.isel(id_dim=stn_id).max() - -plt.plot( thresh_obs.threshold, normalised_event_count ) -plt.ylim([20,40]) -plt.xlim([0.0,0.4]) -plt.title(f"time over threshold") -plt.xlabel('Analysis threshold (m)') -plt.ylabel('normalised event count (%)') -plt.plot(thresh_obs.threshold[22], normalised_event_count[22], 'r+', markersize=20) -``` - - - - - [] - - - - - -![png](/COAsT/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_65_1.png) - - - - -## Other TidegaugeAnalysis methods - -### Calculate errors - -The difference() routine will calculate differences, absolute_differences and squared differenced for all variables. Corresponding new variables are created with names `diff_*`, `abs_diff_*` and `square_diff_*` - - -```python -ntr_diff = tganalysis.difference(ntr_obs.dataset, ntr_mod.dataset) -ssh_diff = tganalysis.difference(obs_new.dataset, model_new.dataset) - -# Take a look -ntr_diff.dataset -``` - - Tidegauge object at 0x56032c641fc0 initialised - Tidegauge object at 0x56032c641fc0 initialised - - - - - -
                - - - - - - - - - - - - - - -
                <xarray.Dataset>
                -Dimensions:          (t_dim: 720, id_dim: 61)
                -Coordinates:
                -  * time             (t_dim) datetime64[ns] 2007-01-01 ... 2007-01-30T23:00:00
                -    site_name        (id_dim) object 'Harwich' 'Mumbles' ... 'N/A' 'N/A'
                -    longitude        (id_dim) float64 1.292 -3.975 -5.158 ... 7.567 350.8 8.717
                -    latitude         (id_dim) float64 51.95 51.57 57.9 ... 58.0 51.53 53.87
                -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
                - - - -We can then easily get mean errors, MAE and MSE - - - -```python -mean_stats = ntr_diff.dataset.mean(dim="t_dim", skipna=True) -``` ```python diff --git a/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md b/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md index add63d91c25..6f7161f5380 100644 --- a/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md @@ -53,7 +53,7 @@ Initiate a TideGauge object, if a filename is passed it assumes it is a GESLA ty tg = coast.Tidegauge() ``` - Tidegauge object at 0x565340358fc0 initialised + Tidegauge object at 0x55af750be980 initialised Specify the data read as a High Low Water dataset. @@ -63,12 +63,6 @@ Specify the data read as a High Low Water dataset. tg.read_hlw(filnam, date_start, date_end) ``` - /usr/share/miniconda/envs/coast/lib/python3.8/site-packages/coast/data/tidegauge.py:564: 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. - dataset = dataset.assign_coords(time=("time", time)) - /usr/share/miniconda/envs/coast/lib/python3.8/site-packages/coast/data/tidegauge.py:564: 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. - dataset = dataset.assign_coords(time=("time", time)) - - Show dataset. If timezone is specified then it is presented as requested, otherwise uses UTC. @@ -80,6 +74,123 @@ tg.show(timezone="Europe/London") Try the TideGauge.show() method: + + --------------------------------------------------------------------------- + + IndexError Traceback (most recent call last) + + Cell In[6], line 2 + 1 print("Try the TideGauge.show() method:") + ----> 2 tg.show(timezone="Europe/London") + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/coast/data/tidegauge.py:599, in Tidegauge.show(self, timezone) + 594 else: # display timezone aware times + 595 for ssh, idx in enumerate(self.dataset.ssh): + 596 # debug('time:', self.dataset.time[i].values, + 597 debug( + 598 "time (" + timezone + "):", + --> 599 general_utils.day_of_week(self.dataset.time[idx].values), + 600 np.datetime_as_string(self.dataset.time[idx], unit="m", timezone=pytz.timezone(timezone)), + 601 "height:", + 602 ssh.values, + 603 "m", + 604 ) + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataarray.py:851, in DataArray.__getitem__(self, key) + 848 return self._getitem_coord(key) + 849 else: + 850 # xarray-style array indexing + --> 851 return self.isel(indexers=self._item_key_to_dict(key)) + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataarray.py:1453, in DataArray.isel(self, indexers, drop, missing_dims, **indexers_kwargs) + 1450 indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "isel") + 1452 if any(is_fancy_indexer(idx) for idx in indexers.values()): + -> 1453 ds = self._to_temp_dataset()._isel_fancy( + 1454 indexers, drop=drop, missing_dims=missing_dims + 1455 ) + 1456 return self._from_temp_dataset(ds) + 1458 # Much faster algorithm for when all indexers are ints, slices, one-dimensional + 1459 # lists, or zero or one-dimensional np.ndarray's + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:2947, in Dataset._isel_fancy(self, indexers, drop, missing_dims) + 2944 valid_indexers = dict(self._validate_indexers(indexers, missing_dims)) + 2946 variables: dict[Hashable, Variable] = {} + -> 2947 indexes, index_variables = isel_indexes(self.xindexes, valid_indexers) + 2949 for name, var in self.variables.items(): + 2950 if name in index_variables: + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/indexes.py:1796, in isel_indexes(indexes, indexers) + 1792 def isel_indexes( + 1793 indexes: Indexes[Index], + 1794 indexers: Mapping[Any, Any], + 1795 ) -> tuple[dict[Hashable, Index], dict[Hashable, Variable]]: + -> 1796 return _apply_indexes(indexes, indexers, "isel") + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/indexes.py:1780, in _apply_indexes(indexes, args, func) + 1778 index_args = {k: v for k, v in args.items() if k in index_dims} + 1779 if index_args: + -> 1780 new_index = getattr(index, func)(index_args) + 1781 if new_index is not None: + 1782 new_indexes.update({k: new_index for k in index_vars}) + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/indexes.py:726, in PandasIndex.isel(self, indexers) + 722 if not isinstance(indxr, slice) and is_scalar(indxr): + 723 # scalar indexer: drop index + 724 return None + --> 726 return self._replace(self.index[indxr]) + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pandas/core/indexes/base.py:5196, in Index.__getitem__(self, key) + 5193 else: + 5194 key = np.asarray(key, dtype=bool) + -> 5196 result = getitem(key) + 5197 # Because we ruled out integer above, we always get an arraylike here + 5198 if result.ndim > 1: + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pandas/core/arrays/datetimelike.py:370, in DatetimeLikeArrayMixin.__getitem__(self, key) + 362 """ + 363 This getitem defers to the underlying array, which by-definition can + 364 only handle list-likes, slices, and integer scalars + 365 """ + 366 # Use cast as we know we will get back a DatetimeLikeArray or DTScalar, + 367 # but skip evaluating the Union at runtime for performance + 368 # (see https://github.com/pandas-dev/pandas/pull/44624) + 369 result = cast( + --> 370 "Union[DatetimeLikeArrayT, DTScalarOrNaT]", super().__getitem__(key) + 371 ) + 372 if lib.is_scalar(result): + 373 return result + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pandas/core/arrays/_mixins.py:280, in NDArrayBackedExtensionArray.__getitem__(self, key) + 277 # error: Incompatible types in assignment (expression has type "ExtensionArray", + 278 # variable has type "Union[int, slice, ndarray]") + 279 key = extract_array(key, extract_numpy=True) # type: ignore[assignment] + --> 280 key = check_array_indexer(self, key) + 281 result = self._ndarray[key] + 282 if lib.is_scalar(result): + + + File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pandas/core/indexers/utils.py:553, in check_array_indexer(array, indexer) + 549 raise ValueError( + 550 "Cannot index with an integer indexer containing NA values" + 551 ) from err + 552 else: + --> 553 raise IndexError("arrays used as indices must be of integer or boolean type") + 555 return indexer + + + IndexError: arrays used as indices must be of integer or boolean type + + Do a basic plot of these points. @@ -90,7 +201,7 @@ tg.dataset.plot.scatter(x="time", y="ssh") - + @@ -173,7 +284,7 @@ HLW = tg.get_tide_table_times(np.datetime64("2020-10-13 12:48"), winsize=2, meth HLW = tg.get_tide_table_times(np.datetime64("2020-10-13 12:48"), winsize=1, method="nearest_1") ``` - /usr/share/miniconda/envs/coast/lib/python3.8/site-packages/coast/data/tidegauge.py:665: 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. + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/coast/data/tidegauge.py:676: 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. 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zXp70ctg%NluYF($Wv$Zdu(Ise3F&lSw{l6VA`r01;niYpi~+JQjQMFC7o>7m|DO93 g$;JNb1~!iW;^w<7b>bt;K-zZh?1duJ>1+4@0M5squ>b%7 literal 0 HcmV?d00001 diff --git a/static/tidetable_tutorial_files/tidetable_tutorial_14_1.png b/static/tidetable_tutorial_files/tidetable_tutorial_14_1.png index f0655eb01a4e37be1aaea1f29a190ea685a659f8..1f3b28d2533cf25faacfffa3b33c1bb0079b2bcf 100644 GIT binary patch delta 43 ycmcbScq4Iwn}UU&p^idENl8JmmA-y%Vo5 Date: Fri, 17 Nov 2023 16:46:32 +0000 Subject: [PATCH 18/32] Updated Altimetry notebook pages from coast repo. --- .../Notebooks/Altimetry/altimetry_tutorial.md | 20 ++++++++++++------ .../altimetry_tutorial_15_1.png | Bin 60952 -> 60952 bytes .../altimetry_tutorial_16_0.png | Bin 59188 -> 59188 bytes .../altimetry_tutorial_17_0.png | Bin 60713 -> 60713 bytes 4 files changed, 14 insertions(+), 6 deletions(-) diff --git a/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md b/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md index 43bbcb7b4cf..550d45e2a4a 100644 --- a/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md @@ -40,8 +40,16 @@ 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. + warnings.warn( + + ././config/example_altimetry.json - Altimetry object at 0x561fb52d1fc0 initialised + Altimetry object at 0x55ee9dd90980 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. + warnings.warn( ### Subsetting @@ -57,7 +65,7 @@ ind = ind[::4] altimetry = altimetry.isel(t_dim=ind) ``` - Subsetting Altimetry object at 0x561fb52d1fc0 indices in [-10, 10], [45, 60] + Subsetting Altimetry object at 0x55ee9dd90980 indices in [-10, 10], [45, 60] ### Model interpolation @@ -74,7 +82,7 @@ altimetry.obs_operator(nemo, mod_var_name="ssh", time_interp="nearest") # to see for yourself. ``` - Interpolating Gridded object at 0x561fb52d1fc0 "ssh" with time_interp "nearest" + Interpolating Gridded object at 0x55ee9dd90980 "ssh" with time_interp "nearest" @@ -93,7 +101,7 @@ altimetry.obs_operator(nemo, mod_var_name="ssh", time_interp="nearest") stats = altimetry.basic_stats("ocean_tide_standard_name", "interp_ssh") ``` - Altimetry object at 0x561fb52d1fc0 initialised + Altimetry object at 0x55ee9dd90980 initialised @@ -121,7 +129,7 @@ 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 0x561fb52d1fc0 initialised + Altimetry object at 0x55ee9dd90980 initialised ### Plotting data @@ -134,7 +142,7 @@ crps = altimetry.crps(nemo, model_var_name="ssh", obs_var_name="ocean_tide_stand fig, ax = altimetry.quick_plot("ocean_tide_standard_name") ``` - /usr/share/miniconda/envs/coast/lib/python3.8/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip warnings.warn(f'Downloading: {url}', DownloadWarning) diff --git a/static/altimetry_tutorial_files/altimetry_tutorial_15_1.png b/static/altimetry_tutorial_files/altimetry_tutorial_15_1.png index 15de65558ac4d27b686543217902ee668c5455a0..4d11624b8ab845ed3ee59fb2e3cdf9795bb6885e 100644 GIT binary patch delta 45 zcmbPnhk3>w<_T^J7J7y{3K=CO1;tkS`nicE1v&X8Ihjd%`9w<_T^J=6c3D3K=CO1;tkS`nicE1v&X8Ihjd%`98w B5+ncs delta 45 zcmdmTj(N*D<_T^J=6c3D3K=CO1;tkS`nicE1v&X8Ihjd%`9DP B5|ID^ diff --git a/static/altimetry_tutorial_files/altimetry_tutorial_17_0.png b/static/altimetry_tutorial_files/altimetry_tutorial_17_0.png index 4a96ae7655785569fea0065f2f68eb1250455b43..2854a421e187af3779d6c14f66e2d2233e931f2a 100644 GIT binary patch delta 45 zcmZ2^i+SZO<_T^J7J7y{3K=CO1;tkS`nicE1v&X8Ihjd%`9`> From 98c0cc0f580b087df4b6579254d2094696fa5ca7 Mon Sep 17 00:00:00 2001 From: MarkDownBot Date: Fri, 17 Nov 2023 16:46:42 +0000 Subject: [PATCH 19/32] Updated Profile notebook pages from coast repo. --- .../Profile/introduction_to_profile_class.md | 20 +++++++++++------- .../Profile/wod_bgc_ragged_example.md | 4 ++-- .../introduction_to_profile_class_12_1.png | Bin 55733 -> 55733 bytes .../introduction_to_profile_class_33_1.png | Bin 27958 -> 27958 bytes .../introduction_to_profile_class_34_1.png | Bin 23717 -> 23717 bytes .../introduction_to_profile_class_38_0.png | Bin 56509 -> 56509 bytes .../introduction_to_profile_class_44_0.png | Bin 54646 -> 54646 bytes .../introduction_to_profile_class_46_1.png | Bin 34007 -> 34007 bytes .../introduction_to_profile_class_56_1.png | Bin 20486 -> 20486 bytes .../introduction_to_profile_class_59_1.png | Bin 17559 -> 17559 bytes .../introduction_to_profile_class_63_1.png | Bin 18623 -> 18623 bytes .../wod_bgc_ragged_example_17_0.png | Bin 21785 -> 21785 bytes .../wod_bgc_ragged_example_17_1.png | Bin 23707 -> 23707 bytes 13 files changed, 14 insertions(+), 10 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 c82ad28f2c3..c9763754a32 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 @@ -139,7 +139,7 @@ Have a look inside the `profile.py` class to see what it can do profile.plot_map() ``` - /usr/share/miniconda/envs/coast/lib/python3.8/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip warnings.warn(f'Downloading: {url}', DownloadWarning) @@ -179,6 +179,10 @@ 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. + warnings.warn( + + #### Create a landmask array in Gridded In this example we add a `landmask` variable to the `Gridded` dataset. When this is present, the `obs_operator` will use this to interpolation to the @@ -200,9 +204,9 @@ nemo.dataset = nemo.dataset.rename({"depth_0": "depth"}) # profile methods will model_profiles = profile.obs_operator(nemo) ``` - /usr/share/miniconda/envs/coast/lib/python3.8/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. mod_profiles["interp_lag"] = (["id_dim"], interp_lag) - /usr/share/miniconda/envs/coast/lib/python3.8/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. mod_profiles["interp_lag"] = (["id_dim"], interp_lag) @@ -311,7 +315,7 @@ differences.dataset.diff_temperature.plot() - + @@ -339,7 +343,7 @@ plt.colorbar( label='temperature diff (obs-model)') - + @@ -539,7 +543,7 @@ plt.legend() - + @@ -579,7 +583,7 @@ plt.colorbar() - + @@ -620,7 +624,7 @@ 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 ac0355a044e..b82cad0757e 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 @@ -109,9 +109,9 @@ model_profiles = wod_profile_sub.obs_operator(seasia_bgc) #model_profiles.dataset # uncomment to print data object summary ``` - /usr/share/miniconda/envs/coast/lib/python3.8/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. mod_profiles["interp_lag"] = (["id_dim"], interp_lag) - /usr/share/miniconda/envs/coast/lib/python3.8/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. 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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. + warnings.warn( + + 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: @@ -87,7 +91,7 @@ plt.pcolormesh(nemo_t.dataset.longitude, nemo_t.dataset.latitude, nemo_t.dataset - + @@ -161,7 +165,7 @@ plt.ylabel("latitude") plt.show() ``` - /tmp/ipykernel_6144/2498690501.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_6194/2498690501.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. plt.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 6bfd89f4172..9a4b9afbde7 100644 --- a/content/en/docs/Examples/Notebooks/Gridded/potential_energy_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Gridded/potential_energy_tutorial.md @@ -467,10 +467,10 @@ 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 00d77d3eaaf..3ab0602cbd9 100644 --- a/content/en/docs/Examples/Notebooks/Gridded/pycnocline_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Gridded/pycnocline_tutorial.md @@ -103,7 +103,7 @@ strat = coast.GriddedStratification(nemo_nwes_t) strat.construct_pycnocline_vars( nemo_nwes_t, nemo_nwes_w ) ``` - /usr/share/miniconda/envs/coast/lib/python3.8/site-packages/xarray/core/computation.py:771: RuntimeWarning: invalid value encountered in sqrt + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/computation.py:821: RuntimeWarning: invalid value encountered in sqrt result_data = func(*input_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 3cb2b1688b6..5a2e5ec021c 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 @@ -56,7 +56,7 @@ plt.ylabel("latitude") plt.show() ``` - /tmp/ipykernel_6245/1161426776.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_6316/1161426776.py:2: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. 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.../mask_maker_tutorial_37_1.png | Bin 29141 -> 29141 bytes .../quiver_tutorial_14_2.png | Bin 0 -> 545750 bytes .../seasonal_decomp_example_17_1.png | Bin 38543 -> 38727 bytes .../seasonal_decomp_example_7_1.png | Bin 68301 -> 68490 bytes .../seasonal_decomp_example_9_1.png | Bin 34320 -> 33785 bytes 17 files changed, 65 insertions(+), 53 deletions(-) create mode 100644 static/quiver_tutorial_files/quiver_tutorial_14_2.png diff --git a/content/en/docs/Examples/Notebooks/General/climatology_tutorial.md b/content/en/docs/Examples/Notebooks/General/climatology_tutorial.md index 64e28b3cfc7..4fdf19248f5 100644 --- a/content/en/docs/Examples/Notebooks/General/climatology_tutorial.md +++ b/content/en/docs/Examples/Notebooks/General/climatology_tutorial.md @@ -51,6 +51,10 @@ 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. + warnings.warn( + + 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 f132c982d70..d4f55d5b88d 100644 --- a/content/en/docs/Examples/Notebooks/General/eof_tutorial.md +++ b/content/en/docs/Examples/Notebooks/General/eof_tutorial.md @@ -70,7 +70,7 @@ eof_data.EOF.sel(mode=[1,2,3,4]).plot.pcolormesh(col='mode',col_wrap=2,x='longit - + @@ -88,7 +88,7 @@ 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 9eceda5246e..a65dc79baa0 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 @@ -39,6 +39,10 @@ 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. + warnings.warn( + + ### We can export the whole xr.DataSet to a netCDF file Other file formats are available. From the documentation: - NETCDF4: Data is stored in an HDF5 file, using netCDF4 API features. 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 fd0a0b75d02..2cac9fd66e1 100644 --- a/content/en/docs/Examples/Notebooks/General/mask_maker_tutorial.md +++ b/content/en/docs/Examples/Notebooks/General/mask_maker_tutorial.md @@ -179,7 +179,7 @@ mask_xr.mask.sum(dim='dim_mask').plot(levels=(1,2,3,4)) - + @@ -611,9 +611,9 @@ 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...

                  • @@ -720,7 +720,7 @@ plt.legend() - + @@ -752,7 +752,7 @@ mask_mean_SST.plot() - + @@ -780,7 +780,7 @@ plt.legend() - + 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 6069367fe64..97cc2189d08 100644 --- a/content/en/docs/Examples/Notebooks/General/seasonal_decomp_example.md +++ b/content/en/docs/Examples/Notebooks/General/seasonal_decomp_example.md @@ -42,6 +42,10 @@ config = root + "./config/example_nemo_grid_t.json" grd = 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. + warnings.warn( + + 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. @@ -71,7 +75,7 @@ temperature = xr.DataArray( ) ``` - /tmp/ipykernel_6237/2929135463.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_6317/2929135463.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. temperature = xr.DataArray( @@ -90,7 +94,7 @@ temperature[0,0,:,:].plot() - + @@ -111,7 +115,7 @@ temperature[:,0,200,200].plot() - [] + [] @@ -529,7 +533,7 @@ 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
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z15_`>A3z*~&Y=2#{SQrQM8##y!g(eOn9`Q8d8gUD zxIs~T_P+-77c$5B{x+kpK@Af#Gcy=@!difny`24I{?D5?g}T#E zW&VDa<&nfnCNi__{|;20!=eSd4BDkDn!ya*ia!&66t}uwB}@HJ4YeR@q&vg`ZVqv+ z!2|aV3=CwQV%2l-N9N647oK(Ooxq&v&> z;=Lz<@acbygMx#n!Q^2FHwY=1+Q2>MauL7kzjCwtniqsm!^9;dHnz4fE>Nndc?H6J z{}|H())VM3lTc;&-H;cm{6w4=)vH4R%1s0)_jUebuU&P_p83ZX0_Q-mq>;rTN zp-4jMp=seCqO0ZuNV6I~hRPAQC|`;tkg)VCE2C8UH*&!tgwZ7Junj9opK` zVhq~^tUnYGa{*;v3)bTa^b}504<0<=h5J%Ck|KiFLW(dZ4>I9TRNmEo24{#sG7lCP zd@|-ZPv!Gz2up~Fikc2nQ&O6g*Arirj}L~cg26LDl!qa*f{5;)@jTi3?UO_6w8a1(&8YR|4_24#^InKye=Tbvx zV-oz7%B^k%22n?U*jyr@5HnogDB%3pe;=yvu^|%|AD^jH$_ Date: Wed, 6 Dec 2023 12:33:28 +0000 Subject: [PATCH 22/32] Updated Altimetry notebook pages from coast repo. --- .../Notebooks/Altimetry/altimetry_tutorial.md | 32 +++++++++++++----- .../altimetry_tutorial_15_1.png | Bin 60952 -> 60952 bytes .../altimetry_tutorial_16_0.png | Bin 59188 -> 59188 bytes .../altimetry_tutorial_17_0.png | Bin 60713 -> 60713 bytes 4 files changed, 24 insertions(+), 8 deletions(-) diff --git a/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md b/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md index 550d45e2a4a..eeef2910b0c 100644 --- a/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md @@ -25,6 +25,25 @@ fn_altimetry = dn_files + "coast_example_altimetry_data.nc" fn_altimetry_config = root + "./config/example_altimetry.json" ``` + /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 + + ### Load data @@ -41,15 +60,13 @@ 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. - warnings.warn( ././config/example_altimetry.json - Altimetry object at 0x55ee9dd90980 initialised + Altimetry object at 0x560476e69980 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. - warnings.warn( ### Subsetting @@ -65,7 +82,7 @@ ind = ind[::4] altimetry = altimetry.isel(t_dim=ind) ``` - Subsetting Altimetry object at 0x55ee9dd90980 indices in [-10, 10], [45, 60] + Subsetting Altimetry object at 0x560476e69980 indices in [-10, 10], [45, 60] ### Model interpolation @@ -82,7 +99,7 @@ altimetry.obs_operator(nemo, mod_var_name="ssh", time_interp="nearest") # to see for yourself. ``` - Interpolating Gridded object at 0x55ee9dd90980 "ssh" with time_interp "nearest" + Interpolating Gridded object at 0x560476e69980 "ssh" with time_interp "nearest" @@ -101,7 +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 0x55ee9dd90980 initialised + Altimetry object at 0x560476e69980 initialised @@ -129,7 +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 0x55ee9dd90980 initialised + Altimetry object at 0x560476e69980 initialised ### Plotting data @@ -143,7 +160,6 @@ fig, ax = altimetry.quick_plot("ocean_tide_standard_name") ``` /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip - warnings.warn(f'Downloading: {url}', DownloadWarning) diff --git a/static/altimetry_tutorial_files/altimetry_tutorial_15_1.png b/static/altimetry_tutorial_files/altimetry_tutorial_15_1.png index 4d11624b8ab845ed3ee59fb2e3cdf9795bb6885e..be9733e827154c91f847095cf1986a73f171b1af 100644 GIT binary patch delta 43 zcmbPnhk3>w<_R8hMmh=^B_##LR{Hw6i6sR&`6W4-NqYH3>G}%4{~g(ww&^VZax@a2 delta 43 zcmbPnhk3>w<_R8hhB^uvB_##LR{Hw6i6sR&`6W4-NqYH3>H3$}2-|E-+w>LyZBG%6 diff --git a/static/altimetry_tutorial_files/altimetry_tutorial_16_0.png b/static/altimetry_tutorial_files/altimetry_tutorial_16_0.png index 54bb691b73c9acdc5a6e76130db01dd0f5e84c68..e2f0ff2e416f8116fb694161787f662ea5e32563 100644 GIT binary patch delta 43 zcmdmTj(N*D<_R8hMmh=^B_##LR{Hw6i6sR&`6W4-NqYH3>G}%4{~g(wcJ3JfcMB5F delta 43 zcmdmTj(N*D<_R8hhB^uvB_##LR{Hw6i6sR&`6W4-NqYH3>H3$}2-|E-JNFC#awZYJ diff --git a/static/altimetry_tutorial_files/altimetry_tutorial_17_0.png b/static/altimetry_tutorial_files/altimetry_tutorial_17_0.png index 2854a421e187af3779d6c14f66e2d2233e931f2a..b33e1bacaa13a9b06e063eee4c100b3b6891629c 100644 GIT binary patch delta 43 zcmZ2^i+SZO<_R8hMmh=^B_##LR{Hw6i6sR&`6W4-NqYH3>G}%4{~g(wcK8hdcQz8t delta 43 zcmZ2^i+SZO<_R8hhB^uvB_##LR{Hw6i6sR&`6W4-NqYH3>H3$}2-|E-JNyO!a#0bx From 86a03f9439be9903396f3fdf321f0776e66106ad Mon Sep 17 00:00:00 2001 From: MarkDownBot Date: Wed, 6 Dec 2023 12:34:50 +0000 Subject: [PATCH 23/32] Updated Profile notebook pages from coast repo. --- .../Profile/introduction_to_profile_class.md | 33 +++++++++++++----- .../Profile/wod_bgc_ragged_example.md | 21 +++++++++-- .../introduction_to_profile_class_12_1.png | Bin 55733 -> 55733 bytes .../introduction_to_profile_class_33_1.png | Bin 27958 -> 27958 bytes .../introduction_to_profile_class_34_1.png | Bin 23717 -> 23717 bytes .../introduction_to_profile_class_38_0.png | Bin 56509 -> 56509 bytes .../introduction_to_profile_class_44_0.png | Bin 54646 -> 54646 bytes .../introduction_to_profile_class_46_1.png | Bin 34007 -> 34007 bytes .../introduction_to_profile_class_56_1.png | Bin 20486 -> 20486 bytes .../introduction_to_profile_class_59_1.png | Bin 17559 -> 17559 bytes .../introduction_to_profile_class_63_1.png | Bin 18623 -> 18623 bytes .../wod_bgc_ragged_example_17_0.png | Bin 21785 -> 21785 bytes .../wod_bgc_ragged_example_17_1.png | Bin 23707 -> 23707 bytes 13 files changed, 43 insertions(+), 11 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 c9763754a32..2a9cb983aa7 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 @@ -77,6 +77,25 @@ import numpy as np import matplotlib.pyplot as plt ``` + /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 + + We can create a new Profile object easily: @@ -140,7 +159,6 @@ profile.plot_map() ``` /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip - warnings.warn(f'Downloading: {url}', DownloadWarning) @@ -180,7 +198,6 @@ nemo = coast.Gridded(fn_nemo_dat, fn_nemo_dom, multiple=True, config=fn_nemo_con ``` /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. - warnings.warn( #### Create a landmask array in Gridded @@ -205,9 +222,7 @@ model_profiles = profile.obs_operator(nemo) ``` /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. - mod_profiles["interp_lag"] = (["id_dim"], interp_lag) /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. - mod_profiles["interp_lag"] = (["id_dim"], interp_lag) Now that we have interpolated the model onto Profiles, we have a new Profile @@ -315,7 +330,7 @@ differences.dataset.diff_temperature.plot() - + @@ -343,7 +358,7 @@ plt.colorbar( label='temperature diff (obs-model)') - + @@ -543,7 +558,7 @@ plt.legend() - + @@ -583,7 +598,7 @@ plt.colorbar() - + @@ -624,7 +639,7 @@ 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 b82cad0757e..04a314f5cd6 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 @@ -27,6 +27,25 @@ path_examples = "./example_files/" path_config = "./config/" ``` + /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 + + ### load and preprocess profile and model data @@ -110,9 +129,7 @@ model_profiles = wod_profile_sub.obs_operator(seasia_bgc) ``` /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. - mod_profiles["interp_lag"] = (["id_dim"], interp_lag) /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. - mod_profiles["interp_lag"] = (["id_dim"], interp_lag) Remove any points that are far from model. diff --git a/static/introduction_to_profile_class_files/introduction_to_profile_class_12_1.png b/static/introduction_to_profile_class_files/introduction_to_profile_class_12_1.png index 645c339885cfe1e8384014c4eabe22432094c7d0..996bcc88b35af0072b3294152970fa77fd2600bc 100644 GIT binary patch delta 43 zcmdnGnR)AG<_R8hMmh=^B_##LR{Hw6i6sR&`6W4-NqYH3>G}%4{~g(w=6@3aZO9T2 delta 43 zcmdnGnR)AG<_R8hhB^uvB_##LR{Hw6i6sR&`6W4-NqYH3>H3$}2-|E-^S=oIXyXw6 diff --git a/static/introduction_to_profile_class_files/introduction_to_profile_class_33_1.png b/static/introduction_to_profile_class_files/introduction_to_profile_class_33_1.png index 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zcmdl#k#YY-#t9yBhB^uvB_##LR{Hw6i6sR&`6W4-NqYH3>H3$}2-|E-i}U~hU_=l@ diff --git a/static/wod_bgc_ragged_example_files/wod_bgc_ragged_example_17_0.png b/static/wod_bgc_ragged_example_files/wod_bgc_ragged_example_17_0.png index 792a8f664ea924f29a4a997e45a7c2f3125b4eef..1bc088abe5ef4b0047ca72fc2f7802098ec55b55 100644 GIT binary patch delta 43 zcmbQaigD&D#t9yBMmh=^B_##LR{Hw6i6sR&`6W4-NqYH3>G}%4{~g(wwmAdH3$}2-|E-+Z+M_T$>P& diff --git a/static/wod_bgc_ragged_example_files/wod_bgc_ragged_example_17_1.png b/static/wod_bgc_ragged_example_files/wod_bgc_ragged_example_17_1.png index c400c3610b2f3360243a01cbf36d5ee8344bdebe..89084ea5acfdbea0056969ca8814c949e76fef90 100644 GIT binary patch delta 43 zcmbQelX3P=#t9yBMmh=^B_##LR{Hw6i6sR&`6W4-NqYH3>G}%4{~g(wW*P$kW$_VA delta 43 zcmbQelX3P=#t9yBhB^uvB_##LR{Hw6i6sR&`6W4-NqYH3>H3$}2-|E-GmQZNVGIyE From 1dfbb7b9d800531562f4356b96818f9fa7ff8191 Mon Sep 17 00:00:00 2001 From: MarkDownBot Date: Wed, 6 Dec 2023 12:34:55 +0000 Subject: [PATCH 24/32] Updated General notebook pages from coast repo. --- .../Notebooks/General/climatology_tutorial.md | 20 +++- .../Notebooks/General/eof_tutorial.md | 23 ++++- .../General/export_to_netcdf_tutorial.md | 20 +++- .../Notebooks/General/mask_maker_tutorial.md | 55 +++++++---- .../General/seasonal_decomp_example.md | 91 +++++++++++------- .../eof_tutorial_files/eof_tutorial_12_1.png | Bin 162048 -> 162048 bytes .../eof_tutorial_files/eof_tutorial_13_1.png | Bin 43922 -> 43922 bytes .../mask_maker_tutorial_18_0.png | Bin 26049 -> 26049 bytes .../mask_maker_tutorial_20_0.png | Bin 21524 -> 21524 bytes .../mask_maker_tutorial_21_1.png | Bin 17259 -> 17259 bytes .../mask_maker_tutorial_33_1.png | Bin 47155 -> 47155 bytes .../mask_maker_tutorial_36_1.png | Bin 11760 -> 11760 bytes .../mask_maker_tutorial_37_1.png | Bin 29141 -> 29141 bytes .../quiver_tutorial_14_3.png | Bin 0 -> 545750 bytes .../seasonal_decomp_example_17_1.png | Bin 38727 -> 38502 bytes .../seasonal_decomp_example_7_1.png | Bin 68490 -> 68699 bytes .../seasonal_decomp_example_9_1.png | Bin 33785 -> 33650 bytes 17 files changed, 152 insertions(+), 57 deletions(-) create mode 100644 static/quiver_tutorial_files/quiver_tutorial_14_3.png diff --git a/content/en/docs/Examples/Notebooks/General/climatology_tutorial.md b/content/en/docs/Examples/Notebooks/General/climatology_tutorial.md index 4fdf19248f5..54653560bc1 100644 --- a/content/en/docs/Examples/Notebooks/General/climatology_tutorial.md +++ b/content/en/docs/Examples/Notebooks/General/climatology_tutorial.md @@ -26,6 +26,25 @@ processes will automatically be used. 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.Climatology.make_climatology(). Calculates mean over a given period of time. This doesn't take different years into account, unless using the @@ -52,7 +71,6 @@ 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. - warnings.warn( 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 d4f55d5b88d..832a4e28282 100644 --- a/content/en/docs/Examples/Notebooks/General/eof_tutorial.md +++ b/content/en/docs/Examples/Notebooks/General/eof_tutorial.md @@ -26,6 +26,25 @@ fn_nemo_dom = dn_files + "coast_example_nemo_domain.nc" fn_nemo_config = root + "./config/example_nemo_grid_t.json" ``` + /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 + + ### Loading data @@ -70,7 +89,7 @@ eof_data.EOF.sel(mode=[1,2,3,4]).plot.pcolormesh(col='mode',col_wrap=2,x='longit - + @@ -88,7 +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 a65dc79baa0..c95ea7cab90 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 @@ -19,6 +19,25 @@ import coast import xarray as xr ``` + /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 + + ### Now define some file paths @@ -40,7 +59,6 @@ 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. - warnings.warn( ### 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 2cac9fd66e1..d948f4457e2 100644 --- a/content/en/docs/Examples/Notebooks/General/mask_maker_tutorial.md +++ b/content/en/docs/Examples/Notebooks/General/mask_maker_tutorial.md @@ -27,6 +27,25 @@ import matplotlib.colors as colors # colormap fiddling import xarray as xr ``` + /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 + + ```python # set some paths @@ -179,7 +198,7 @@ mask_xr.mask.sum(dim='dim_mask').plot(levels=(1,2,3,4)) - + @@ -611,9 +630,9 @@ 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...

                    • @@ -720,7 +739,7 @@ plt.legend() - + @@ -752,7 +771,7 @@ mask_mean_SST.plot() - + @@ -780,7 +799,7 @@ plt.legend() - + 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 97cc2189d08..ac5f8784147 100644 --- a/content/en/docs/Examples/Notebooks/General/seasonal_decomp_example.md +++ b/content/en/docs/Examples/Notebooks/General/seasonal_decomp_example.md @@ -42,8 +42,24 @@ config = root + "./config/example_nemo_grid_t.json" grd = coast.Gridded(fn_nemo_dat, fn_nemo_dom, config=config) ``` + /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 /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. - warnings.warn( 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. @@ -75,8 +91,7 @@ temperature = xr.DataArray( ) ``` - /tmp/ipykernel_6317/2929135463.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. - temperature = xr.DataArray( + /tmp/ipykernel_2925/2929135463.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 @@ -94,7 +109,7 @@ temperature[0,0,:,:].plot() - + @@ -115,7 +130,7 @@ temperature[:,0,200,200].plot() - [] + [] @@ -142,6 +157,12 @@ proc_data = coast.Process_data() grd = proc_data.seasonal_decomposition(temperature, 2, model="additive", period=6, extrapolate_trend="freq") ``` + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/coast/_utils/logging_util.py:21: DeprecationWarning: ProcessData is deprecated: Please use the 'ProcessData' class instead. This name will change in the next release. + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/coast/_utils/process_data.py:114: DeprecationWarning: Supplying chunks as dimension-order tuples is deprecated. It will raise an error in the future. Instead use a dict with dimension names as keys. + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/coast/_utils/process_data.py:116: DeprecationWarning: Supplying chunks as dimension-order tuples is deprecated. It will raise an error in the future. Instead use a dict with dimension names as keys. + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/coast/_utils/process_data.py:118: DeprecationWarning: Supplying chunks as dimension-order tuples is deprecated. It will raise an error in the future. Instead use a dict with dimension names as keys. + + The returned xarray Dataset contains the decomposed time series (trend, seasonal, residual) as dask arrays @@ -533,7 +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
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.../potential_energy_tutorial_11_1.png | Bin 59458 -> 59458 bytes .../potential_energy_tutorial_21_1.png | Bin 5725 -> 5725 bytes .../pycnocline_tutorial_17_1.png | Bin 55223 -> 55223 bytes .../pycnocline_tutorial_17_2.png | Bin 53291 -> 53291 bytes .../seasia_dic_example_plot_tutorial_8_1.png | Bin 119133 -> 119133 bytes .../transect_tutorial_10_0.png | Bin 25674 -> 25674 bytes .../transect_tutorial_20_0.png | Bin 32601 -> 32601 bytes .../transect_tutorial_22_0.png | Bin 23092 -> 23092 bytes 17 files changed, 122 insertions(+), 12 deletions(-) diff --git a/content/en/docs/Examples/Notebooks/Gridded/contour_tutorial.md b/content/en/docs/Examples/Notebooks/Gridded/contour_tutorial.md index abc8b2e19fb..23a403bef1e 100644 --- a/content/en/docs/Examples/Notebooks/Gridded/contour_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Gridded/contour_tutorial.md @@ -45,6 +45,25 @@ fn_config_u_grid = root + "./config/example_nemo_grid_u.json" fn_config_v_grid = root + "./config/example_nemo_grid_v.json" ``` + /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 + + ### Extract isobath contour between two points and create contour object. 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 542c7e12c1b..f15fc40e8a5 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 @@ -37,6 +37,25 @@ fn_nemo_dom = dn_files + "coast_example_nemo_domain.nc" fn_config_t_grid = root + "./config/example_nemo_grid_t.json" ``` + /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 + + 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. @@ -45,7 +64,6 @@ nemo_t = coast.Gridded(fn_data = fn_nemo_dat, fn_domain = fn_nemo_dom, config=fn ``` /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. - warnings.warn( 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: @@ -91,7 +109,7 @@ plt.pcolormesh(nemo_t.dataset.longitude, nemo_t.dataset.latitude, nemo_t.dataset - + @@ -165,8 +183,7 @@ plt.ylabel("latitude") plt.show() ``` - /tmp/ipykernel_6194/2498690501.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. - plt.pcolormesh( + /tmp/ipykernel_2626/2498690501.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 9a4b9afbde7..d4279af0f0b 100644 --- a/content/en/docs/Examples/Notebooks/Gridded/potential_energy_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Gridded/potential_energy_tutorial.md @@ -21,6 +21,25 @@ import matplotlib.colors as colors # colormap fiddling import xarray as xr ``` + /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 + + ```python # set some paths @@ -467,10 +486,10 @@ 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 3ab0602cbd9..874d42cf1bc 100644 --- a/content/en/docs/Examples/Notebooks/Gridded/pycnocline_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Gridded/pycnocline_tutorial.md @@ -21,6 +21,25 @@ import matplotlib.pyplot as plt import matplotlib.colors as colors # colormap fiddling ``` + /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 + + ```python # set some paths @@ -104,7 +123,6 @@ 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 - result_data = func(*input_data) ### 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 5a2e5ec021c..6e2985753d6 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 @@ -16,6 +16,25 @@ import coast import matplotlib.pyplot as plt ``` + /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 + + ### Define file paths for data @@ -56,8 +75,7 @@ plt.ylabel("latitude") plt.show() ``` - /tmp/ipykernel_6316/1161426776.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. - plt.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. diff --git a/content/en/docs/Examples/Notebooks/Gridded/transect_tutorial.md b/content/en/docs/Examples/Notebooks/Gridded/transect_tutorial.md index 396a045b54e..255ff4beebb 100644 --- a/content/en/docs/Examples/Notebooks/Gridded/transect_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Gridded/transect_tutorial.md @@ -25,6 +25,25 @@ import coast import matplotlib.pyplot as plt ``` + /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 + + ### Define filepaths for data and configuration diff --git a/static/contour_tutorial_files/contour_tutorial_12_1.png b/static/contour_tutorial_files/contour_tutorial_12_1.png index f86de98881bec6a5bc580c1bf9a68581ebd6d016..be5778903fb6b9ae2dce37556e2082e3e93e24e3 100644 GIT binary patch delta 46 zcmezPgZ1MN)(IYRMmh=^B_##LR{Hw6i6sR&`6W4-NqYH3>G}%4{~c*e+nUD6T>}8n CWfNck delta 46 zcmezPgZ1MN)(IYRhB^uvB_##LR{Hw6i6sR&`6W4-NqYH3>H3$}2-`HKZB1k3t^okZ CO%hK4 diff --git a/static/introduction_to_gridded_class_files/introduction_to_gridded_class_11_1.png b/static/introduction_to_gridded_class_files/introduction_to_gridded_class_11_1.png index 4ef0bbf0de8bd9552f19304f0cb338fe6ab53642..883a5c5856afe9206e493f79690680e83faf21ca 100644 GIT binary patch delta 46 zcmaF8o8|RxmI)qmMmh=^B_##LR{Hw6i6sR&`6W4-NqYH3>G}%4{~c*e+nUDMDhmL) C)DrUm delta 46 zcmaF8o8|RxmI)qmhB^uvB_##LR{Hw6i6sR&`6W4-NqYH3>H3$}2-`HKZB1irl?4E? CybG}%4{~c*e+nUDs{Q>~5 C4HM7+ delta 46 zcmcb+i2d#&_6Z(xhB^uvB_##LR{Hw6i6sR&`6W4-NqYH3>H3$}2-`HKZB1kRegOcc C^%B1T diff --git a/static/potential_energy_tutorial_files/potential_energy_tutorial_11_1.png b/static/potential_energy_tutorial_files/potential_energy_tutorial_11_1.png index 8289a7e132cdddf9d9417b7c14b9f578f1168367..0ac7756bbde5a25dc71766d781e7ee5887b15516 100644 GIT binary patch delta 43 zcmX?ff%(t{<_R8hMmh=^B_##LR{Hw6i6sR&`6W4-NqYH3>G}%4{~g(wcIP<&d{h$t delta 43 zcmX?ff%(t{<_R8hhB^uvB_##LR{Hw6i6sR&`6W4-NqYH3>H3$}2-|E-yYn0XcW)8x diff --git a/static/potential_energy_tutorial_files/potential_energy_tutorial_21_1.png b/static/potential_energy_tutorial_files/potential_energy_tutorial_21_1.png index 2555d90700ef7d814aa2cd060b97821e7c3a296e..60a89225f2f910a5b2f10ae6bacdf23a78494e29 100644 GIT binary patch delta 41 xcmcbsb601Ahn$g)LPkkRL9vy-er{q(K~8>2PG*u`eo?x2PG*u`eo?yqr8UAf8`Hjv0su#*5FP*k diff --git a/static/pycnocline_tutorial_files/pycnocline_tutorial_17_1.png b/static/pycnocline_tutorial_files/pycnocline_tutorial_17_1.png index 7284b2788c5bf67b244b3f18688e40b7eabecf2e..46e4af63bb6a0ff97b5cff8d18a6faa5606ac56f 100644 GIT binary patch delta 43 zcmdnKo_YIv<_R8hMmh=^B_##LR{Hw6i6sR&`6W4-NqYH3>G}%4{~g(w7I+;1Z7dQC delta 43 zcmdnKo_YIv<_R8hhB^uvB_##LR{Hw6i6sR&`6W4-NqYH3>H3$}2-|E-3%m{hXh#tG diff --git a/static/pycnocline_tutorial_files/pycnocline_tutorial_17_2.png b/static/pycnocline_tutorial_files/pycnocline_tutorial_17_2.png index 627c338a8f824c162aae89a1263272b15b189cbf..163f6fb609a5e5f7dbe10736e5a0a2421f3d8b1b 100644 GIT binary patch delta 43 zcmZ3zfO+)-<_R8hMmh=^B_##LR{Hw6i6sR&`6W4-NqYH3>G}%4{~g(wcJw>|WoZ&Y delta 43 zcmZ3zfO+)-<_R8hhB^uvB_##LR{Hw6i6sR&`6W4-NqYH3>H3$}2-|E-J9-`fV1yAc diff --git a/static/seasia_dic_example_plot_tutorial_files/seasia_dic_example_plot_tutorial_8_1.png b/static/seasia_dic_example_plot_tutorial_files/seasia_dic_example_plot_tutorial_8_1.png index 0a31c7935267043c2f0335591e9168a8f6348306..07ce38f597462b54152da4ad7d310d6641348abc 100644 GIT binary patch delta 46 zcmcb+i2d#&_6Z(xMmh=^B_##LR{Hw6i6sR&`6W4-NqYH3>G}%4{~c*e+nUDs{Q>~5 C4HM7+ delta 46 zcmcb+i2d#&_6Z(xhB^uvB_##LR{Hw6i6sR&`6W4-NqYH3>H3$}2-`HKZB1kRegOcc C^%B1T diff --git a/static/transect_tutorial_files/transect_tutorial_10_0.png b/static/transect_tutorial_files/transect_tutorial_10_0.png index 5a7108a75deb7eedbbd20972e5a20844496550ef..d8799a55a83e8f751c85bbdff9200f5c70bf1e93 100644 GIT binary patch delta 43 zcmX?gg7MS|#t9yBMmh=^B_##LR{Hw6i6sR&`6W4-NqYH3>G}%4{~g(w_9Phqdr}g9 delta 43 zcmX?gg7MS|#t9yBhB^uvB_##LR{Hw6i6sR&`6W4-NqYH3>H3$}2-|E-dy)(Qc5M-D diff --git a/static/transect_tutorial_files/transect_tutorial_20_0.png b/static/transect_tutorial_files/transect_tutorial_20_0.png index ad9f0ec87a3489201f6be89a3a52721d801047c1..86281d4101c7731e3fb84d83a5d0e3d8fbc610fa 100644 GIT binary patch delta 43 zcmcclkMZU|#t9yBMmh=^B_##LR{Hw6i6sR&`6W4-NqYH3>G}%4{~g(w_PGuKk$w|0 delta 43 zcmcclkMZU|#t9yBhB^uvB_##LR{Hw6i6sR&`6W4-NqYH3>H3$}2-|E-`&lOk#t9yBMmh=^B_##LR{Hw6i6sR&`6W4-NqYH3>G}%4{~g(wb}kYCZKe_m delta 43 zcmdn8g>lOk#t9yBhB^uvB_##LR{Hw6i6sR&`6W4-NqYH3>H3$}2-|E-I~NH6Xu%Nq From 52ca04e70b1e77d0c184f49837650f0a171355c0 Mon Sep 17 00:00:00 2001 From: tobfer Date: Thu, 7 Dec 2023 14:44:06 +0000 Subject: [PATCH 26/32] update notebook names --- .github/workflows/markdown-general.yml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/.github/workflows/markdown-general.yml b/.github/workflows/markdown-general.yml index 06023d85eae..c9ab6eff47d 100644 --- a/.github/workflows/markdown-general.yml +++ b/.github/workflows/markdown-general.yml @@ -44,6 +44,9 @@ jobs: mkdir -p ./example_scripts/notebook_tutorials/markdown/general ls ./example_scripts/notebook_tutorials pip install . + sudo apt-get update + sudo apt-get install -y libgeos-dev + pip install cartopy==0.21.0 mv config ./example_scripts/notebook_tutorials/runnable_notebooks/general/config bash notebook_to_md.sh general - name: convert notebooks @@ -57,6 +60,8 @@ jobs: mv ./markdown/general/climatology_tutorial.md $GITHUB_WORKSPACE/site/content/en/docs/Examples/Notebooks/General/ mv ./markdown/general/dask_wrapper_template_tutorial.md $GITHUB_WORKSPACE/site/content/en/docs/Examples/Notebooks/General/ mv ./markdown/general/mask_maker_tutorial.md $GITHUB_WORKSPACE/site/content/en/docs/Examples/Notebooks/General/ + mv ./markdown/general/polar_plotting.md $GITHUB_WORKSPACE/site/content/en/docs/Examples/Notebooks/General/ + mv ./markdown/general/zarr_files.md $GITHUB_WORKSPACE/site/content/en/docs/Examples/Notebooks/General/ echo "Move images" rsync -r ./markdown_images/general/ $GITHUB_WORKSPACE/site/static From 33be9261fd52389147badc84a72d4e6666967d83 Mon Sep 17 00:00:00 2001 From: MarkDownBot Date: Thu, 7 Dec 2023 14:44:56 +0000 Subject: [PATCH 27/32] Updated Altimetry notebook pages from coast repo. --- .../Examples/Notebooks/Altimetry/altimetry_tutorial.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md b/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md index eeef2910b0c..657c6b12b53 100644 --- a/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Altimetry/altimetry_tutorial.md @@ -63,7 +63,7 @@ altimetry = coast.Altimetry(fn_altimetry, config=fn_altimetry_config) ././config/example_altimetry.json - Altimetry object at 0x560476e69980 initialised + Altimetry object at 0x560d5d3e2980 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. @@ -82,7 +82,7 @@ ind = ind[::4] altimetry = altimetry.isel(t_dim=ind) ``` - Subsetting Altimetry object at 0x560476e69980 indices in [-10, 10], [45, 60] + Subsetting Altimetry object at 0x560d5d3e2980 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 0x560476e69980 "ssh" with time_interp "nearest" + Interpolating Gridded object at 0x560d5d3e2980 "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 0x560476e69980 initialised + Altimetry object at 0x560d5d3e2980 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 0x560476e69980 initialised + Altimetry object at 0x560d5d3e2980 initialised ### Plotting data From da7c4272e9181c6130e6ab162ac0b7f01eb0dc38 Mon Sep 17 00:00:00 2001 From: MarkDownBot Date: Thu, 7 Dec 2023 14:45:18 +0000 Subject: [PATCH 28/32] Updated Altimetry notebook pages from coast repo. --- .../tidegauge_validation_tutorial.md | 3910 ++++++++++++----- .../Notebooks/Tidegauge/tidetable_tutorial.md | 151 +- .../tidegauge_validation_tutorial_18_1.png | Bin 36722 -> 58858 bytes .../tidegauge_validation_tutorial_20_0.png | Bin 51113 -> 61082 bytes .../tidegauge_validation_tutorial_22_0.png | Bin 0 -> 51113 bytes .../tidegauge_validation_tutorial_24_1.png | Bin 50674 -> 66904 bytes .../tidegauge_validation_tutorial_26_1.png | Bin 0 -> 50674 bytes .../tidegauge_validation_tutorial_37_0.png | Bin 0 -> 63286 bytes .../tidegauge_validation_tutorial_46_0.png | Bin 0 -> 75161 bytes .../tidegauge_validation_tutorial_59_0.png | Bin 76315 -> 96720 bytes .../tidegauge_validation_tutorial_61_0.png | Bin 0 -> 76315 bytes .../tidegauge_validation_tutorial_65_1.png | Bin 29266 -> 24568 bytes .../tidegauge_validation_tutorial_67_1.png | Bin 0 -> 29266 bytes .../tidetable_tutorial_14_1.png | Bin 12504 -> 17176 bytes 14 files changed, 2920 insertions(+), 1141 deletions(-) create mode 100644 static/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_22_0.png create mode 100644 static/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_26_1.png create mode 100644 static/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_37_0.png create mode 100644 static/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_46_0.png create mode 100644 static/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_61_0.png create mode 100644 static/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_67_1.png 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 3e9d10fcc13..7419f7b4967 100644 --- a/content/en/docs/Examples/Notebooks/Tidegauge/tidegauge_validation_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Tidegauge/tidegauge_validation_tutorial.md @@ -34,6 +34,25 @@ import datetime import matplotlib.pyplot as plt ``` + /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 + + ### Define paths @@ -52,6 +71,7 @@ if 1: fn_dat = dir + "coast_example_nemo_data.nc" fn_config = "./config/example_nemo_grid_t.json" fn_tidegauge = dir + "tide_gauges/lowestoft-p024-uk-bodc" + fn_tidegauge_bodc = dir + "tide_gauges/LIV2010.txt" fn_tg = dir + "tide_gauges/coast_example_tidegauges.nc" # These are a collection (xr.DataSet) of tidegauge observations. Created for this demonstration, they are synthetic. ``` @@ -64,7 +84,7 @@ We can create our empty tidegauge object: tidegauge = coast.Tidegauge() ``` - Tidegauge object at 0x563dc355e980 initialised + Tidegauge object at 0x55a06f621980 initialised The `Tidegauge` class contains multiple methods for reading different typical @@ -75,7 +95,7 @@ To read a gesla file between two dates, we can use: ```python date0 = datetime.datetime(2007,1,10) date1 = datetime.datetime(2007,1,12) -tidegauge.read_gesla_v3(fn_tidegauge, date_start = date0, date_end = date1) +tidegauge.read_gesla(fn_tidegauge, date_start = date0, date_end = date1) ``` A Tidegauge object is a type of Timeseries object, so it has the form: @@ -457,11 +477,11 @@ Coordinates: time (t_dim) datetime64[ns] 2007-01-10 ... 2007-01-12 longitude (id_dim) float64 1.751 latitude (id_dim) float64 52.47 - id_name (id_dim) <U9 'Lowestoft' + site_name (id_dim) <U9 'Lowestoft' 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
                        • -```python -# Create a landmask array and put it into the nemo object. -# Here, using the bottom_level == 0 variable from the domain file is enough. -nemo.dataset["landmask"] = nemo.dataset.bottom_level == 0 - -# Then do the interpolation -tidegauge_from_model = obs.obs_operator(nemo, time_interp='nearest') -``` - - Calculating spatial indices. - Calculating time indices. - Indexing model data at tide gauge locations.. - Calculating interpolation distances. - Interpolating in time... - Tidegauge object at 0x563dc355e980 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. - - -```python -# Cut down data to be only in 2007 to match model data. -start_date = datetime.datetime(2007, 1, 1) -end_date = datetime.datetime(2007, 1, 31) -obs = obs.time_slice(start_date, end_date) -``` - - Tidegauge object at 0x563dc355e980 initialised - - -### Interpolate model data onto obs locations +You can do the same for BODC data: ```python -model_timeseries = obs.obs_operator(nemo) - -# Take a look -model_timeseries.dataset +date0 = np.datetime64("2020-10-11 07:59") +date1 = np.datetime64("2020-10-20 20:21") +tidegauge.read_bodc(fn_tidegauge_bodc, date0, date1) +tidegauge.dataset ``` - Calculating spatial indices. - Calculating time indices. - Indexing model data at tide gauge locations.. - Calculating interpolation distances. - Interpolating in time... - Tidegauge object at 0x563dc355e980 initialised - - @@ -1121,620 +934,2905 @@ dl.xr-attrs { fill: currentColor; }
                          <xarray.Dataset>
                          -Dimensions:        (z_dim: 51, axis_nbounds: 2, t_dim: 720, id_dim: 61)
                          +Dimensions:    (id_dim: 1, t_dim: 914)
                           Coordinates:
                          -    longitude      (id_dim) float32 1.444 -4.0 -5.333 ... 7.666 -9.111 8.777
                          -    latitude       (id_dim) float32 51.93 51.53 58.0 51.67 ... 58.0 51.53 53.87
                          -    depth          (z_dim, id_dim) float32 0.1001 0.2183 0.2529 ... 27.32 10.11
                          -  * time           (t_dim) datetime64[ns] 2007-01-01 ... 2007-01-30T23:00:00
                          -Dimensions without coordinates: z_dim, axis_nbounds, t_dim, id_dim
                          +    time       (t_dim) datetime64[ns] 2020-10-11T08:00:00 ... 2020-10-20T20:1...
                          +    longitude  (id_dim) float64 -3.018
                          +    latitude   (id_dim) float64 53.45
                          +    site_name  (id_dim) <U25 'liverpool,_gladstone_dock'
                          +Dimensions without coordinates: id_dim, t_dim
                           Data variables:
                          -    deptht_bounds  (z_dim, axis_nbounds) float32 0.0 6.157 ... 5.924e+03
                          -    ssh            (t_dim, id_dim) float32 nan nan nan ... 0.3721 -0.0752 0.7412
                          -    temperature    (t_dim, z_dim, id_dim) float32 nan nan nan ... nan nan nan
                          -    bathymetry     (id_dim) float32 10.0 21.81 6.075 15.56 ... 17.8 14.06 10.0
                          -    e1             (id_dim) float32 7.618e+03 7.686e+03 ... 7.686e+03 7.285e+03
                          -    e2             (id_dim) float32 7.414e+03 7.414e+03 ... 7.414e+03 7.414e+03
                          -    e3_0           (z_dim, id_dim) float32 0.2002 0.4365 0.5059 ... 0.541 0.2002
                          -    bottom_level   (id_dim) float32 50.0 50.0 12.0 32.0 ... 44.0 17.0 26.0 50.0
                          -    landmask       (id_dim) bool False False False False ... False False False
                          -    interp_dist    (id_dim) float64 10.56 4.33 15.65 6.018 ... 5.835 4.96 3.957
                          +    ssh        (id_dim, t_dim) float64 5.369 5.196 5.032 ... 1.668 1.568 1.545
                          +    qc_flags   (id_dim, t_dim) <U1 'M' 'M' 'M' 'M' 'M' ... 'M' 'M' 'M' 'M' 'M'
                           Attributes:
                          -    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...
                          -    NCO:          4.4.7
                          • time
                            PandasIndex
                            PandasIndex(DatetimeIndex(['2007-01-01 00:00:00', '2007-01-01 01:00:00',
                            -               '2007-01-01 02:00:00', '2007-01-01 03:00:00',
                            -               '2007-01-01 04:00:00', '2007-01-01 05:00:00',
                            -               '2007-01-01 06:00:00', '2007-01-01 07:00:00',
                            -               '2007-01-01 08:00:00', '2007-01-01 09:00:00',
                            -               ...
                            -               '2007-01-30 14:00:00', '2007-01-30 15:00:00',
                            -               '2007-01-30 16:00:00', '2007-01-30 17:00:00',
                            -               '2007-01-30 18:00:00', '2007-01-30 19:00:00',
                            -               '2007-01-30 20:00:00', '2007-01-30 21:00:00',
                            -               '2007-01-30 22:00:00', '2007-01-30 23:00:00'],
                            -              dtype='datetime64[ns]', name='time', length=720, freq=None))
                        • 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
                        • +An example data variable could be ssh, or ntr (non-tidal residual). This object can also be used for other instrument types, not just tide gauges. For example moorings. + +Every id index for this object should use the same time coordinates. Therefore, timeseries need to be aligned before being placed into the object. If there is any padding needed, then NaNs should be used. NaNs should also be used for quality control/data rejection. -```python -stn_id=26 # pick a gauge station +For the rest of our examples, we will use data from multiple tide gauges +on the same time dimension. It will be read in from a simple netCDF file: -plt.subplot(2,1,1) -model_timeseries.dataset.ssh.isel(id_dim=stn_id).plot() # rename time dimension to enable automatic x-axis labelling -plt.subplot(2,1,2) -obs.dataset.ssh.isel(id_dim=stn_id).swap_dims({'t_dim':'time'}).plot() # rename time dimension to enable automatic x-axis labelling -plt.tight_layout() -plt.show() +```python +tt = xr.open_dataset(fn_tg) +obs = coast.Tidegauge(dataset=tt) ``` + Tidegauge object at 0x55a06f621980 initialised - -![png](/COAsT/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_35_0.png) - -We can see that the structure for the new dataset `model_timeseries`, generated from the gridded model simulation, is that of a `tidegauge` object. NB in the example simulation data the ssh variable is output as 5-day means. So it is not particulatly useful for high frequency validation but serves as a demonstration of the workflow. +```python +# Create the object and then inset the netcdf dataset +tt = xr.open_dataset(fn_tg) +obs = coast.Tidegauge(dataset=tt) +obs.dataset = obs.dataset.set_coords("time") +``` -## Tidegauge analysis methods + Tidegauge object at 0x55a06f621980 initialised -For a good comparison, we would like to make sure that both the observed and -modelled `Tidegauge` objects contain the same missing values. `TidegaugeAnalysis` -contains a routine for ensuring this. First create our analysis object: +## Quick plotting to visualise Tidegauge data -```python -tganalysis = coast.TidegaugeAnalysis() -``` +Tidegauge has ready made quick plotting routines for viewing time series and tide gauge location. To look at the tide gauge location: ```python -# This routine searches for missing values in each dataset and applies them -# equally to each corresponding dataset -obs_new, model_new = tganalysis.match_missing_values(obs.dataset.ssh, model_timeseries.dataset.ssh) +fig, ax = obs.plot_on_map() ``` - Tidegauge object at 0x563dc355e980 initialised - Tidegauge object at 0x563dc355e980 initialised - + /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip -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. -### Harmonic Analysis & Non tidal-Residuals -The `Tidegauge` object contains some routines which make harmonic analysis and -the calculation/comparison of non-tidal residuals easier. Harmonic analysis is -done using the `utide` package. Please see [here](https://pypi.org/project/UTide/) for more info. + +![png](/COAsT/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_18_1.png) + -First we can use the `TidegaugeAnalysis` class to do a harmonic analysis. Suppose -we have two `Tidegauge` objects called `obs` and `model_timeseries` generated from tidegauge observations and model simulations respectively. -Then subtract means from all the time series +There is also a slightly expanded version where you can plot multiple tidegauge objects, included as a list, and also the colour (if it is a dataarray with one value per location). ```python -# Subtract means from all time series -obs_new = tganalysis.demean_timeseries(obs_new.dataset) -model_new = tganalysis.demean_timeseries(model_new.dataset) - -# Now you have equivalent and comparable sets of time series that can be -# easily compared. +# plot a list tidegauge datasets (here repeating obs for the point of demonstration) and colour +fig, ax = obs.plot_on_map_multiple([obs,obs], color_var_str="latitude") ``` - Tidegauge object at 0x563dc355e980 initialised - Tidegauge object at 0x563dc355e980 initialised - + +![png](/COAsT/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_20_0.png) + -```python -``` +Time series can be plotted using `matplotlib.pyplot` methods. However `xarray` has its own plotting wrappers that can be used, which offers some conveniences with labelling ```python -plt.figure() -plt.plot( model_new.dataset.time, model_new.dataset.ssh.isel(id_dim=stn_id), - label='model - demeaned', color='orange' ) -plt.plot( obs_new.dataset.time, obs_new.dataset.ssh.isel(id_dim=stn_id) , - label='obs - demeaned', color='blue' ) -plt.title(f'model and observed timeseries : {obs_new.dataset.site_name.isel(id_dim=stn_id).coords}') -plt.legend() -plt.xticks(rotation=45) +stn_id=26 # pick a gauge station + +plt.subplot(2,1,1) +obs.dataset.ssh[stn_id].plot() # rename time dimension to enable automatic x-axis labelling + +plt.subplot(2,1,2) +obs.dataset.ssh[stn_id].swap_dims({'t_dim':'time'}).plot() # rename time dimension to enable automatic x-axis labelling +plt.tight_layout() plt.show() ``` -![png](/COAsT/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_44_0.png) +![png](/COAsT/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_22_0.png) -Then we can apply the harmonic analysis (though the example data is too short for this example to be that meaningful and the model data is only saved as 5-day means! Nevertheless we proceed): +Or you can use the `Tidegauge.plot_timeseries()` method, in which start and end dates can also be specified. -### Calculate non tidal residuals +```python +obs.isel(id_dim=stn_id).plot_timeseries(date_start=np.datetime64('2007-01-01'), + date_end = np.datetime64('2007-01-31') ) +``` -First, do a harmonic analysis. This routine uses utide -```python -ha_mod = tganalysis.harmonic_analysis_utide(model_new.dataset.ssh, min_datapoints=1) -ha_obs = tganalysis.harmonic_analysis_utide(obs_new.dataset.ssh, min_datapoints=1) -``` + (
                          , + ) - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. - solve: matrix prep ... solution ... done. -The `harmonic_analysis_utide` routine returns a list of `utide` structure object, -one for each `id_dim` in the `Tidegauge` object. It can be passed any of the -arguments that go to `utide`. It also has an additional argument `min_datapoints` -which determines a minimum number of data points for the harmonics analysis. -If a tidegauge `id_dim` has less than this number, it will not return an analysis. -Now, create new `TidegaugeMultiple` objects containing reconstructed tides: + +![png](/COAsT/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_24_1.png) + + + +## Basic manipulation: subsetting + plotting + + + +We can do some simple spatial and temporal manipulations of this data: ```python -tide_mod = tganalysis.reconstruct_tide_utide(model_new.dataset.time, ha_mod) -tide_obs = tganalysis.reconstruct_tide_utide(obs_new.dataset.time, ha_obs) +# Cut out a time window +obs_cut = obs.time_slice( date0 = datetime.datetime(2007, 1, 1), date1 = datetime.datetime(2007,1,31)) + +# Cut out geographical boxes +obs_upper = obs_cut.subset_indices_lonlat_box(lonbounds = [0, 3], + latbounds = [50, 55]) +obs_lower = obs_cut.subset_indices_lonlat_box(lonbounds = [-9, -3], + latbounds = [55.5, 59]) +#fig, ax = obs_cut.plot_on_map() +fig, ax = obs_upper.plot_on_map_multiple([obs_upper, obs_lower], color_var_str="latitude") ``` - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. - prep/calcs ... done. + Tidegauge object at 0x55a06f621980 initialised + Tidegauge object at 0x55a06f621980 initialised + Tidegauge object at 0x55a06f621980 initialised + + + + +![png](/COAsT/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_26_1.png) + +### Gridded model comparison - --------------------------------------------------------------------------- +To compare our observations to the model, we will interpolate a model variable to the same time and geographical space as the tidegauge. This is done using the `obs_operator()` method. - KeyError Traceback (most recent call last) +But first load some gridded data and manipulate some variable names for convenience - Cell In[23], line 1 - ----> 1 tide_mod = tganalysis.reconstruct_tide_utide(model_new.dataset.time, ha_mod) - 2 tide_obs = tganalysis.reconstruct_tide_utide(obs_new.dataset.time, ha_obs) +```python +nemo = coast.Gridded(fn_dat, fn_dom, multiple=True, config=fn_config) +# Rename depth_0 to be depth +nemo.dataset = nemo.dataset.rename({"depth_0": "depth"}) +#nemo.dataset = nemo.dataset[["ssh", "landmask"]] +``` - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/coast/diagnostics/tidegauge_analysis.py:163, in TidegaugeAnalysis.reconstruct_tide_utide(cls, data_array, utide_solution_list, constit, output_name) - 161 ds_out = xr.Dataset(data_array.coords) - 162 ds_out[output_name] = (["id_dim", "t_dim"], reconstructed) - --> 163 ds_out = ds_out.assign_coords(time=("t_dim", time.data)) - 165 return Tidegauge(dataset=ds_out) + /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. - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/common.py:621, in DataWithCoords.assign_coords(self, coords, **coords_kwargs) - 618 else: - 619 results = self._calc_assign_results(coords_combined) - --> 621 data.coords.update(results) - 622 return data +interpolate model onto obs locations +``` +tidegauge_from_model = obs.obs_operator(nemo, time_interp='nearest') +``` +Doing this would create a new interpolated tidegauge called `tidegauge_from_model` +Take a look at `tidegauge_from_model.dataset` to see for yourself. If a `landmask` +variable is present in the `Gridded` dataset then the nearest wet points will +be taken. Otherwise, just the nearest point is taken. If `landmask` is required +but not present you will need to insert it into the dataset yourself. For nemo +data, you could use the `bottom_level` or `mbathy` variables to do this. E.g: - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/coordinates.py:552, in Coordinates.update(self, other) - 542 other_coords = create_coords_with_default_indexes( - 543 getattr(other, "variables", other) - 544 ) - 546 # Discard original indexed coordinates prior to merge allows to: - 547 # - fail early if the new coordinates don't preserve the integrity of existing - 548 # multi-coordinate indexes - 549 # - drop & replace coordinates without alignment (note: we must keep indexed - 550 # coordinates extracted from the DataArray objects passed as values to - 551 # `other` - if any - as those are still used for aligning the old/new coordinates) - --> 552 coords_to_align = drop_indexed_coords(set(other_coords) & set(other), self) - 554 coords, indexes = merge_coords( - 555 [coords_to_align, other_coords], - 556 priority_arg=1, - 557 indexes=coords_to_align.xindexes, - 558 ) - 560 # special case for PandasMultiIndex: updating only its dimension coordinate - 561 # is still allowed but depreciated. - 562 # It is the only case where we need to actually drop coordinates here (multi-index levels) - 563 # TODO: remove when removing PandasMultiIndex's dimension coordinate. - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/coordinates.py:918, in drop_indexed_coords(coords_to_drop, coords) - 911 raise ValueError( - 912 f"cannot drop or update coordinate(s) {idx_drop_coords_str}, which would corrupt " - 913 f"the following index built from coordinates {idx_coords_str}:\n" - 914 f"{idx}" - 915 ) - 917 for k in idx_drop_coords: - --> 918 del new_variables[k] - 919 del new_indexes[k] - 921 return Coordinates._construct_direct(coords=new_variables, indexes=new_indexes) +```python +# Create a landmask array and put it into the nemo object. +# Here, using the bottom_level == 0 variable from the domain file is enough. +nemo.dataset["landmask"] = nemo.dataset.bottom_level == 0 +# Then do the interpolation +tidegauge_from_model = obs.obs_operator(nemo, time_interp='nearest') +``` - KeyError: 'time' + Calculating spatial indices. + Calculating time indices. + Indexing model data at tide gauge locations.. + Calculating interpolation distances. + Interpolating in time... + Tidegauge object at 0x55a06f621980 initialised -Get new TidegaugeMultiple objects containing non tidal residuals: +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. ```python -ntr_mod = tganalysis.calculate_non_tidal_residuals(model_new.dataset.ssh, tide_mod.dataset.reconstructed, apply_filter=False) -ntr_obs = tganalysis.calculate_non_tidal_residuals(obs_new.dataset.ssh, tide_obs.dataset.reconstructed, apply_filter=True, window_length=10, polyorder=2) - -# Take a look -ntr_obs.dataset +# Cut down data to be only in 2007 to match model data. +start_date = datetime.datetime(2007, 1, 1) +end_date = datetime.datetime(2007, 1, 31) +obs = obs.time_slice(start_date, end_date) ``` + Tidegauge object at 0x55a06f621980 initialised + + +### Interpolate model data onto obs locations + - --------------------------------------------------------------------------- +```python +model_timeseries = obs.obs_operator(nemo) - NameError Traceback (most recent call last) +# Take a look +model_timeseries.dataset +``` - Cell In[24], line 1 - ----> 1 ntr_mod = tganalysis.calculate_non_tidal_residuals(model_new.dataset.ssh, tide_mod.dataset.reconstructed, apply_filter=False) - 2 ntr_obs = tganalysis.calculate_non_tidal_residuals(obs_new.dataset.ssh, tide_obs.dataset.reconstructed, apply_filter=True, window_length=10, polyorder=2) - 4 # Take a look + Calculating spatial indices. + Calculating time indices. + Indexing model data at tide gauge locations.. + Calculating interpolation distances. + Interpolating in time... + Tidegauge object at 0x55a06f621980 initialised - NameError: name 'tide_mod' is not defined -The dataset structure is preserved and has created a new variable called `ntr` - non-tidal residual. -By default, this routines will apply `scipy.signal.savgol_filter` to the non-tidal residuals -to remove some noise. This can be switched off using `apply_filter = False`. +
                          + + + + + + + + + + + + + + +
                          <xarray.Dataset>
                          +Dimensions:        (z_dim: 51, axis_nbounds: 2, t_dim: 720, id_dim: 61)
                          +Coordinates:
                          +    longitude      (id_dim) float32 1.444 -4.0 -5.333 ... 7.666 -9.111 8.777
                          +    latitude       (id_dim) float32 51.93 51.53 58.0 51.67 ... 58.0 51.53 53.87
                          +    depth          (z_dim, id_dim) float32 0.1001 0.2183 0.2529 ... 27.32 10.11
                          +  * time           (t_dim) datetime64[ns] 2007-01-01 ... 2007-01-30T23:00:00
                          +Dimensions without coordinates: z_dim, axis_nbounds, t_dim, id_dim
                          +Data variables:
                          +    deptht_bounds  (z_dim, axis_nbounds) float32 0.0 6.157 ... 5.924e+03
                          +    ssh            (t_dim, id_dim) float32 nan nan nan ... 0.3721 -0.0752 0.7412
                          +    temperature    (t_dim, z_dim, id_dim) float32 nan nan nan ... nan nan nan
                          +    bathymetry     (id_dim) float32 10.0 21.81 6.075 15.56 ... 17.8 14.06 10.0
                          +    e1             (id_dim) float32 7.618e+03 7.686e+03 ... 7.686e+03 7.285e+03
                          +    e2             (id_dim) float32 7.414e+03 7.414e+03 ... 7.414e+03 7.414e+03
                          +    e3_0           (z_dim, id_dim) float32 0.2002 0.4365 0.5059 ... 0.541 0.2002
                          +    bottom_level   (id_dim) float32 50.0 50.0 12.0 32.0 ... 44.0 17.0 26.0 50.0
                          +    landmask       (id_dim) bool False False False False ... False False False
                          +    interp_dist    (id_dim) float64 10.56 4.33 15.65 6.018 ... 5.835 4.96 3.957
                          +Attributes:
                          +    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...
                          +    NCO:          4.4.7
                          + + + + +```python +stn_id=26 # pick a gauge station + +plt.subplot(2,1,1) +model_timeseries.dataset.ssh.isel(id_dim=stn_id).plot() # rename time dimension to enable automatic x-axis labelling + +plt.subplot(2,1,2) +obs.dataset.ssh.isel(id_dim=stn_id).swap_dims({'t_dim':'time'}).plot() # rename time dimension to enable automatic x-axis labelling +plt.tight_layout() +plt.show() +``` + + + +![png](/COAsT/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_37_0.png) + + + +We can see that the structure for the new dataset `model_timeseries`, generated from the gridded model simulation, is that of a `tidegauge` object. NB in the example simulation data the ssh variable is output as 5-day means. So it is not particulatly useful for high frequency validation but serves as a demonstration of the workflow. + +## Tidegauge analysis methods + +For a good comparison, we would like to make sure that both the observed and +modelled `Tidegauge` objects contain the same missing values. `TidegaugeAnalysis` +contains a routine for ensuring this. First create our analysis object: + + +```python +tganalysis = coast.TidegaugeAnalysis() +``` + + +```python +# This routine searches for missing values in each dataset and applies them +# equally to each corresponding dataset +obs_new, model_new = tganalysis.match_missing_values(obs.dataset.ssh, model_timeseries.dataset.ssh) +``` + + Tidegauge object at 0x55a06f621980 initialised + Tidegauge object at 0x55a06f621980 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. + +### Harmonic Analysis & Non tidal-Residuals + +The `Tidegauge` object contains some routines which make harmonic analysis and +the calculation/comparison of non-tidal residuals easier. Harmonic analysis is +done using the `utide` package. Please see [here](https://pypi.org/project/UTide/) for more info. + +First we can use the `TidegaugeAnalysis` class to do a harmonic analysis. Suppose +we have two `Tidegauge` objects called `obs` and `model_timeseries` generated from tidegauge observations and model simulations respectively. + +Then subtract means from all the time series + + +```python +# Subtract means from all time series +obs_new = tganalysis.demean_timeseries(obs_new.dataset) +model_new = tganalysis.demean_timeseries(model_new.dataset) + +# Now you have equivalent and comparable sets of time series that can be +# easily compared. +``` + + Tidegauge object at 0x55a06f621980 initialised + Tidegauge object at 0x55a06f621980 initialised + + + +```python + +``` + + +```python +plt.figure() +plt.plot( model_new.dataset.time, model_new.dataset.ssh.isel(id_dim=stn_id), + label='model - demeaned', color='orange' ) +plt.plot( obs_new.dataset.time, obs_new.dataset.ssh.isel(id_dim=stn_id) , + label='obs - demeaned', color='blue' ) +plt.title(f'model and observed timeseries : {obs_new.dataset.site_name.isel(id_dim=stn_id).coords}') +plt.legend() +plt.xticks(rotation=45) +plt.show() +``` + + + +![png](/COAsT/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_46_0.png) + + + +Then we can apply the harmonic analysis (though the example data is too short for this example to be that meaningful and the model data is only saved as 5-day means! Nevertheless we proceed): + +### Calculate non tidal residuals + + + +First, do a harmonic analysis. This routine uses utide + + +```python +ha_mod = tganalysis.harmonic_analysis_utide(model_new.dataset.ssh, min_datapoints=1) +ha_obs = tganalysis.harmonic_analysis_utide(obs_new.dataset.ssh, min_datapoints=1) +``` + + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + solve: matrix prep ... solution ... done. + + +The `harmonic_analysis_utide` routine returns a list of `utide` structure object, +one for each `id_dim` in the `Tidegauge` object. It can be passed any of the +arguments that go to `utide`. It also has an additional argument `min_datapoints` +which determines a minimum number of data points for the harmonics analysis. +If a tidegauge `id_dim` has less than this number, it will not return an analysis. + +Now, create new `TidegaugeMultiple` objects containing reconstructed tides: + + + +```python +tide_mod = tganalysis.reconstruct_tide_utide(model_new.dataset.time, ha_mod) +tide_obs = tganalysis.reconstruct_tide_utide(obs_new.dataset.time, ha_obs) + +``` + + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + Tidegauge object at 0x55a06f621980 initialised + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + prep/calcs ... done. + Tidegauge object at 0x55a06f621980 initialised + + +Get new TidegaugeMultiple objects containing non tidal residuals: + + +```python +ntr_mod = tganalysis.calculate_non_tidal_residuals(model_new.dataset.ssh, tide_mod.dataset.reconstructed, apply_filter=False) +ntr_obs = tganalysis.calculate_non_tidal_residuals(obs_new.dataset.ssh, tide_obs.dataset.reconstructed, apply_filter=True, window_length=10, polyorder=2) + +# Take a look +ntr_obs.dataset +``` + + Tidegauge object at 0x55a06f621980 initialised + Tidegauge object at 0x55a06f621980 initialised + + + + + +
                          + + + + + + + + + + + + + + +
                          <xarray.Dataset>
                          +Dimensions:    (t_dim: 720, id_dim: 61)
                          +Coordinates:
                          +    time       (t_dim) datetime64[ns] 2007-01-01 ... 2007-01-30T23:00:00
                          +    longitude  (id_dim) float64 1.292 -3.975 -5.158 -5.051 ... 7.567 350.8 8.717
                          +    latitude   (id_dim) float64 51.95 51.57 57.9 51.71 ... 58.0 51.53 53.87
                          +    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
                          + + + +The dataset structure is preserved and has created a new variable called `ntr` - non-tidal residual. + +By default, this routines will apply `scipy.signal.savgol_filter` to the non-tidal residuals +to remove some noise. This can be switched off using `apply_filter = False`. + +The Doodson X0 filter is an alternative way of estimating non-tidal residuals. This will return a new Tidegauge() object containing filtered ssh data: + + +```python +dx0 = tganalysis.doodson_x0_filter(obs_new.dataset, "ssh") + +# take a look +dx0.dataset + +``` + + Tidegauge object at 0x55a06f621980 initialised + + + + + +
                          + + + + + + + + + + + + + + +
                          <xarray.Dataset>
                          +Dimensions:    (t_dim: 720, id_dim: 61)
                          +Coordinates:
                          +    longitude  (id_dim) float64 1.292 -3.975 -5.158 -5.051 ... 7.567 350.8 8.717
                          +    latitude   (id_dim) float64 51.95 51.57 57.9 51.71 ... 58.0 51.53 53.87
                          +    site_name  (id_dim) <U25 'Harwich' 'Mumbles' 'Ullapool' ... 'N/A' 'N/A'
                          +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
                          + + + + +```python + +``` + +We can compare these analyses e.g. the observed timeseries against the harmonic reconstruction. Noting we can in principle extend the harmoninc reconstruction beyond the observation time window. + + +```python +plt.figure() +plt.plot( tide_obs.dataset.time, tide_obs.dataset.reconstructed.isel(id_dim=stn_id), label='reconstructed from harmonics', color='orange' ) +plt.plot( obs_new.dataset.time, obs_new.dataset.ssh.isel(id_dim=stn_id) , label='observed', color='blue' ) +plt.legend() +plt.xticks(rotation=45) +plt.show() +``` + + + +![png](/COAsT/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_59_0.png) + + + +We can also look closer at the difference between the observed timeseries and the harmonic reconstruction, that is the non-tidal residual. And we can compare the observed and modelled non-harmonic residual and contrast the different methods of removing the tides. Here we contrast using a harmonic analysis (and creating a "non-tidal residual") with the DoodsonX0 filter. Note that the timeseries is far too short for a sensible analysis and that this is really a demonstration of concept. + + +```python +plt.figure() +plt.subplot(2,1,1) +plt.plot( dx0.dataset.time, dx0.isel(id_dim=stn_id).dataset.ssh, label='obs: doodsonX0 filtered', color='orange' ) +plt.plot( ntr_obs.dataset.time, ntr_obs.isel(id_dim=stn_id).dataset.ntr, label='obs: non-tidal residual', color='blue' ) +plt.title('analysis comparison: non-tidal residual vs doodsonX0') +plt.legend() +plt.xticks(rotation=45) + +plt.subplot(2,1,2) +plt.plot( ntr_mod.dataset.time, ntr_mod.isel(id_dim=stn_id).dataset.ntr, label='model: non-tidal residual', color='g' ) +plt.plot( ntr_obs.dataset.time, ntr_obs.isel(id_dim=stn_id).dataset.ntr, label='obs: non-tidal residual', color='blue' ) +plt.title('model vs observation: non-tidal residual') +plt.legend() +plt.xticks(rotation=45) + +plt.tight_layout() + +``` + + + +![png](/COAsT/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_61_0.png) + + + +### Threshold Statistics for non-tidal residuals + +This is a simple extreme value analysis of whatever data you use. It will count the number of peaks and the total time spent over each threshold provided. It will also count the numbers of daily and monthly maxima over each threshold. To this, a Tidegauge object and an array of thresholds (in metres) should be passed. The method return `peak_count_*`, `time_over_threshold_*`, `dailymax_count_*`, `monthlymax_count_*`: + + + +```python +thresh_mod = tganalysis.threshold_statistics(ntr_mod.dataset, thresholds=np.arange(-2, 2, 0.1)) +thresh_obs = tganalysis.threshold_statistics(ntr_obs.dataset, thresholds=np.arange(-2, 2, 0.1)) +``` + + +```python +# Have a look +thresh_obs +``` + + + + +
                          + + + + + + + + + + + + + + +
                          <xarray.Dataset>
                          +Dimensions:                  (t_dim: 720, id_dim: 61, threshold: 40)
                          +Coordinates:
                          +    longitude                (id_dim) float64 1.292 -3.975 ... 350.8 8.717
                          +    latitude                 (id_dim) float64 51.95 51.57 57.9 ... 51.53 53.87
                          +    site_name                (id_dim) <U25 'Harwich' 'Mumbles' ... 'N/A' 'N/A'
                          +  * threshold                (threshold) float64 -2.0 -1.9 -1.8 ... 1.7 1.8 1.9
                          +Dimensions without coordinates: t_dim, id_dim
                          +Data variables:
                          +    time                     (t_dim) datetime64[ns] 2007-01-01 ... 2007-01-30...
                          +    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
                          + + + + +```python +plt.plot( thresh_obs.threshold, thresh_obs.peak_count_ntr.mean(dim='id_dim') ) +#plt.ylim([0,15]) +plt.title(f"Observed peaks over threshold") +plt.xlabel('Analysis threshold (m)') +plt.ylabel('event count') +``` + + + + + Text(0, 0.5, 'event count') + + + + + +![png](/COAsT/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_65_1.png) + + + +Note that the non-tidal residual is a noisy timeseries (computed as a difference between two timeseries) so peaks do not necessarily correspond to peaks in total water level. For this reason `time_over_threshold_*` can be useful. Below we see that about 28% (y-axis) of the observations of non-tidal residual exceed 20cm (x-axis). Again, threshold statistics are more useful with a longer record. + + +```python +normalised_event_count = 100 * thresh_obs.time_over_threshold_ntr.isel(id_dim=stn_id) / thresh_obs.time_over_threshold_ntr.isel(id_dim=stn_id).max() + +plt.plot( thresh_obs.threshold, normalised_event_count ) +plt.ylim([20,40]) +plt.xlim([0.0,0.4]) +plt.title(f"time over threshold") +plt.xlabel('Analysis threshold (m)') +plt.ylabel('normalised event count (%)') +plt.plot(thresh_obs.threshold[22], normalised_event_count[22], 'r+', markersize=20) +``` + + + + + [] + + + + + +![png](/COAsT/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_67_1.png) + + + + +## Other TidegaugeAnalysis methods + +### Calculate errors + +The difference() routine will calculate differences, absolute_differences and squared differenced for all variables. Corresponding new variables are created with names `diff_*`, `abs_diff_*` and `square_diff_*` ```python -dx0 = tganalysis.doodson_x0_filter(obs_new.dataset, "ssh") - -# take a look -dx0.dataset +ntr_diff = tganalysis.difference(ntr_obs.dataset, ntr_mod.dataset) +ssh_diff = tganalysis.difference(obs_new.dataset, model_new.dataset) +# Take a look +ntr_diff.dataset ``` - Tidegauge object at 0x563dc355e980 initialised + Tidegauge object at 0x55a06f621980 initialised + Tidegauge object at 0x55a06f621980 initialised @@ -2104,15 +4202,32 @@ dl.xr-attrs { fill: currentColor; }
                          <xarray.Dataset>
                          -Dimensions:    (t_dim: 720, id_dim: 61)
                          +Dimensions:          (t_dim: 720, id_dim: 61)
                           Coordinates:
                          -    longitude  (id_dim) float64 1.292 -3.975 -5.158 -5.051 ... 7.567 350.8 8.717
                          -    latitude   (id_dim) float64 51.95 51.57 57.9 51.71 ... 58.0 51.53 53.87
                          -    site_name  (id_dim) object 'Harwich' 'Mumbles' 'Ullapool' ... 'N/A' 'N/A'
                          +  * time             (t_dim) datetime64[ns] 2007-01-01 ... 2007-01-30T23:00:00
                          +    site_name        (id_dim) <U25 'Harwich' 'Mumbles' ... 'N/A' 'N/A'
                          +    longitude        (id_dim) float64 1.292 -3.975 -5.158 ... 7.567 350.8 8.717
                          +    latitude         (id_dim) float64 51.95 51.57 57.9 ... 58.0 51.53 53.87
                           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
                          • time
                            PandasIndex
                            PandasIndex(DatetimeIndex(['2007-01-01 00:00:00', '2007-01-01 01:00:00',
                            +               '2007-01-01 02:00:00', '2007-01-01 03:00:00',
                            +               '2007-01-01 04:00:00', '2007-01-01 05:00:00',
                            +               '2007-01-01 06:00:00', '2007-01-01 07:00:00',
                            +               '2007-01-01 08:00:00', '2007-01-01 09:00:00',
                            +               ...
                            +               '2007-01-30 14:00:00', '2007-01-30 15:00:00',
                            +               '2007-01-30 16:00:00', '2007-01-30 17:00:00',
                            +               '2007-01-30 18:00:00', '2007-01-30 19:00:00',
                            +               '2007-01-30 20:00:00', '2007-01-30 21:00:00',
                            +               '2007-01-30 22:00:00', '2007-01-30 23:00:00'],
                            +              dtype='datetime64[ns]', name='time', length=720, freq=None))
                        • - NameError: name 'ntr_obs' is not defined We can then easily get mean errors, MAE and MSE @@ -2416,18 +4314,6 @@ mean_stats = ntr_diff.dataset.mean(dim="t_dim", skipna=True) ``` - --------------------------------------------------------------------------- - - NameError Traceback (most recent call last) - - Cell In[33], line 1 - ----> 1 mean_stats = ntr_diff.dataset.mean(dim="t_dim", skipna=True) - - - NameError: name 'ntr_diff' is not defined - - - ```python ``` diff --git a/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md b/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md index 6f7161f5380..e6f83093485 100644 --- a/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Tidegauge/tidetable_tutorial.md @@ -27,6 +27,25 @@ import coast import numpy as np ``` + /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 + + Load and plot High and Low Water data. @@ -53,7 +72,7 @@ Initiate a TideGauge object, if a filename is passed it assumes it is a GESLA ty tg = coast.Tidegauge() ``` - Tidegauge object at 0x55af750be980 initialised + Tidegauge object at 0x5560fa4f2980 initialised Specify the data read as a High Low Water dataset. @@ -74,123 +93,6 @@ tg.show(timezone="Europe/London") Try the TideGauge.show() method: - - --------------------------------------------------------------------------- - - IndexError Traceback (most recent call last) - - Cell In[6], line 2 - 1 print("Try the TideGauge.show() method:") - ----> 2 tg.show(timezone="Europe/London") - - - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/coast/data/tidegauge.py:599, in Tidegauge.show(self, timezone) - 594 else: # display timezone aware times - 595 for ssh, idx in enumerate(self.dataset.ssh): - 596 # debug('time:', self.dataset.time[i].values, - 597 debug( - 598 "time (" + timezone + "):", - --> 599 general_utils.day_of_week(self.dataset.time[idx].values), - 600 np.datetime_as_string(self.dataset.time[idx], unit="m", timezone=pytz.timezone(timezone)), - 601 "height:", - 602 ssh.values, - 603 "m", - 604 ) - - - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataarray.py:851, in DataArray.__getitem__(self, key) - 848 return self._getitem_coord(key) - 849 else: - 850 # xarray-style array indexing - --> 851 return self.isel(indexers=self._item_key_to_dict(key)) - - - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataarray.py:1453, in DataArray.isel(self, indexers, drop, missing_dims, **indexers_kwargs) - 1450 indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "isel") - 1452 if any(is_fancy_indexer(idx) for idx in indexers.values()): - -> 1453 ds = self._to_temp_dataset()._isel_fancy( - 1454 indexers, drop=drop, missing_dims=missing_dims - 1455 ) - 1456 return self._from_temp_dataset(ds) - 1458 # Much faster algorithm for when all indexers are ints, slices, one-dimensional - 1459 # lists, or zero or one-dimensional np.ndarray's - - - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/dataset.py:2947, in Dataset._isel_fancy(self, indexers, drop, missing_dims) - 2944 valid_indexers = dict(self._validate_indexers(indexers, missing_dims)) - 2946 variables: dict[Hashable, Variable] = {} - -> 2947 indexes, index_variables = isel_indexes(self.xindexes, valid_indexers) - 2949 for name, var in self.variables.items(): - 2950 if name in index_variables: - - - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/indexes.py:1796, in isel_indexes(indexes, indexers) - 1792 def isel_indexes( - 1793 indexes: Indexes[Index], - 1794 indexers: Mapping[Any, Any], - 1795 ) -> tuple[dict[Hashable, Index], dict[Hashable, Variable]]: - -> 1796 return _apply_indexes(indexes, indexers, "isel") - - - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/indexes.py:1780, in _apply_indexes(indexes, args, func) - 1778 index_args = {k: v for k, v in args.items() if k in index_dims} - 1779 if index_args: - -> 1780 new_index = getattr(index, func)(index_args) - 1781 if new_index is not None: - 1782 new_indexes.update({k: new_index for k in index_vars}) - - - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/xarray/core/indexes.py:726, in PandasIndex.isel(self, indexers) - 722 if not isinstance(indxr, slice) and is_scalar(indxr): - 723 # scalar indexer: drop index - 724 return None - --> 726 return self._replace(self.index[indxr]) - - - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pandas/core/indexes/base.py:5196, in Index.__getitem__(self, key) - 5193 else: - 5194 key = np.asarray(key, dtype=bool) - -> 5196 result = getitem(key) - 5197 # Because we ruled out integer above, we always get an arraylike here - 5198 if result.ndim > 1: - - - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pandas/core/arrays/datetimelike.py:370, in DatetimeLikeArrayMixin.__getitem__(self, key) - 362 """ - 363 This getitem defers to the underlying array, which by-definition can - 364 only handle list-likes, slices, and integer scalars - 365 """ - 366 # Use cast as we know we will get back a DatetimeLikeArray or DTScalar, - 367 # but skip evaluating the Union at runtime for performance - 368 # (see https://github.com/pandas-dev/pandas/pull/44624) - 369 result = cast( - --> 370 "Union[DatetimeLikeArrayT, DTScalarOrNaT]", super().__getitem__(key) - 371 ) - 372 if lib.is_scalar(result): - 373 return result - - - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pandas/core/arrays/_mixins.py:280, in NDArrayBackedExtensionArray.__getitem__(self, key) - 277 # error: Incompatible types in assignment (expression has type "ExtensionArray", - 278 # variable has type "Union[int, slice, ndarray]") - 279 key = extract_array(key, extract_numpy=True) # type: ignore[assignment] - --> 280 key = check_array_indexer(self, key) - 281 result = self._ndarray[key] - 282 if lib.is_scalar(result): - - - File /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/pandas/core/indexers/utils.py:553, in check_array_indexer(array, indexer) - 549 raise ValueError( - 550 "Cannot index with an integer indexer containing NA values" - 551 ) from err - 552 else: - --> 553 raise IndexError("arrays used as indices must be of integer or boolean type") - 555 return indexer - - - IndexError: arrays used as indices must be of integer or boolean type - - Do a basic plot of these points. @@ -201,7 +103,7 @@ tg.dataset.plot.scatter(x="time", y="ssh") - + @@ -271,7 +173,7 @@ HT = tg.get_tide_table_times(np.datetime64("2020-10-13 12:48"), method="nearest_ print("HT:", HT.values, "m at", HT.time.values) ``` - HT: [8.01] m at 2020-10-13T07:59:00.000000000 + HT: 8.01 m at 2020-10-13T07:59:00.000000000 The get_tidetabletimes() method can take extra paremeters such as a window size, an integer number of hours to seek either side of the guess. @@ -282,13 +184,4 @@ HLW = tg.get_tide_table_times(np.datetime64("2020-10-13 12:48"), winsize=2, meth HLW = tg.get_tide_table_times(np.datetime64("2020-10-13 12:48"), winsize=1, method="nearest_1") -``` - - /usr/share/miniconda/envs/coast/lib/python3.10/site-packages/coast/data/tidegauge.py:676: 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. - return xr.DataArray([np.NaN], dims=(time_var), coords={time_var: [time_guess]})[0] - - - -```python - ``` diff --git a/static/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_18_1.png b/static/tidegauge_validation_tutorial_files/tidegauge_validation_tutorial_18_1.png index 4a27f9ac58747fbba67b93b627fb3a3a922e5eb8..a8af48f4acf5c7001cc04ddc93252fc34fbb8e63 100644 GIT binary patch literal 58858 zcmbTe2RN7g|2M3XR4TNLq7V%$WM&kRh*A+kc0xAU8IiJOWhF_Hh^(xljO-+vgzUZd z{W|M+|L^;M-_P+J&+%Nx?>esE)n%OD^L&5a@7H>tK35cEcI}|sK|(^ZOZK9a5(&v> zCK8fO&&kQ~C*dpfH7F z(W9Mq_oJI47uP1P`#DT#gxz4T4AWQ7?r{9P-(%A@Zc1``YW_`{)ZB842ZC$3JV(jS zU%a#ZwlHT)#V)(5Fm@Hy?6+Nxv_>p`27EIGuE*)vSz3qZoZcC8)?ce*mP>G~&8u_P z3uciecRzyvQEXW<7m%PNJxA#|B}dBi@6YI|9{v5V+$K^-n27&Zl1}CJi1>2yQtAI+ 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zP6;t_Jcz4n%{e5_LwVG$6wS+-aHtEZBOar60+QwkIMhenLO@?_*m`B60RfqSEt0oL z_@T1Ym6ZHp#Luv~G&=TsHSisvstHdR%Jg&fD}+IK0@PqAm#!!1 z;R74cH+FRN8HhdwxV8MVXV1*H7hE@P&Gi;2>cGn;mS%0%*?<20S$3+}Q1kco-5x7NhFDmL%LQCZI(!^DLwk;qYSFMK8zE)1=Q5vZZzsfHFD5sL z&yrfyyYVEFx6I>+1qg{`oLIjNjrjQsIH&;l{v;GhzCz4n8FZ56_ylr%LtTYJMpG z*BarKIb{vXOqUMFkkANp!o3W@J`WgGS$N#)LSl4tIq-w;0`vv9)oC+JX!b{PhBgFW7pkIJaFH*bSvCXBTbxQ6;uDbc7SAXr*XTB7nSFINi+uqFe%115Lbq zwclxB3QTL2D`(}eoNa`$zBDy&D7U_Ml1ge)UcaLZY??}(bbvLu&8)X>X#&kbEMr#WDy0V_~OPRqhEgxEtzj@|PGZ38Cr^|~k# z9RRQ%4u5a;4ie1t7=69=QaHL;!rop3cZHNR4`@|;CPg8EW8`Sq(X(9 za%o*wo0f@B4*mgWy%$rV2#LJB7=Yy~p1FKe9@0+67=bYc16UL*%p+nX-b?Qw2ev9J zD+3xDQT6=};DxooffXJ&*UN#$^B+|_vUmiRA@|2+9%-;;0k;zow-#R4eRQKfQb<$m z?dc{^(aE4xlffJXK#l Date: Thu, 7 Dec 2023 14:46:38 +0000 Subject: [PATCH 29/32] 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 2a9cb983aa7..9484140a147 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 @@ -330,7 +330,7 @@ differences.dataset.diff_temperature.plot() - + @@ -358,7 +358,7 @@ plt.colorbar( label='temperature diff (obs-model)') - + @@ -558,7 +558,7 @@ plt.legend() - + @@ -598,7 +598,7 @@ plt.colorbar() - + @@ -639,7 +639,7 @@ plt.colorbar() - + From 20869a2cff2f938bcf2836b1311bd7011cdf5fdd Mon Sep 17 00:00:00 2001 From: MarkDownBot Date: Thu, 7 Dec 2023 14:51:32 +0000 Subject: [PATCH 30/32] Updated General notebook pages from coast repo. --- .../Notebooks/General/eof_tutorial.md | 4 +- .../Notebooks/General/mask_maker_tutorial.md | 36 ++--- .../General/seasonal_decomp_example.md | 126 +++++++++--------- .../polar_plotting_14_1.png | Bin 0 -> 1429942 bytes .../polar_plotting_17_3.png | Bin 0 -> 163443 bytes .../seasonal_decomp_example_17_1.png | Bin 38502 -> 39094 bytes .../seasonal_decomp_example_7_1.png | Bin 68699 -> 69018 bytes .../seasonal_decomp_example_9_1.png | Bin 33650 -> 33428 bytes 8 files changed, 83 insertions(+), 83 deletions(-) create mode 100644 static/polar_plotting_files/polar_plotting_14_1.png create mode 100644 static/polar_plotting_files/polar_plotting_17_3.png diff --git a/content/en/docs/Examples/Notebooks/General/eof_tutorial.md b/content/en/docs/Examples/Notebooks/General/eof_tutorial.md index 832a4e28282..358eaf11052 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 d948f4457e2..8af1bce45b2 100644 --- a/content/en/docs/Examples/Notebooks/General/mask_maker_tutorial.md +++ b/content/en/docs/Examples/Notebooks/General/mask_maker_tutorial.md @@ -198,7 +198,7 @@ mask_xr.mask.sum(dim='dim_mask').plot(levels=(1,2,3,4)) - + @@ -630,9 +630,9 @@ 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...

                          • @@ -739,7 +739,7 @@ plt.legend() - + @@ -771,7 +771,7 @@ mask_mean_SST.plot() - + @@ -799,7 +799,7 @@ plt.legend() - + 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 ac5f8784147..e1202849eae 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_2925/2929135463.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_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. 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/static/polar_plotting_files/polar_plotting_14_1.png b/static/polar_plotting_files/polar_plotting_14_1.png new file mode 100644 index 0000000000000000000000000000000000000000..1d17817779bcc9d234c232fbd5e72b4161606756 GIT binary patch literal 1429942 zcmeFZ1yEgEyDhjwa3>JlJrE$cyF+l-V8Pv;0KtQWV8I~-2oQq1dmy;G1oz;YH#g_M z_rKL$)vvnxb$503U3K74u$RpF*%;&dRw9%YrO}WHks%NWnyie3Dg*+L555Kw;lXd# zn$zpR|9D*`wO!R5%w0W$fj9ndV9b9d#Ovv5eI=fgo*t0Y9FtfcRw{&%Nbm3!R zvHQ;tFgrL~u#n$Abb*T?Im+m`Kp+^#(61+j!Ua~4ClH9Ngs8e_`u?JaD~<*c{INw% z!6h;UMU@C*bL~40D^=JXWEci*k@7qkO$lWj{wzw|FQ)la&6i8Eg+*WJ(#!^Y1K!(K z%zj){fXiIw^;sBPsCTho54}C~IU*6ok;IbtFNb@ay4%z6e;?aRov=@_{(a0wBf(St z`=}toM*S~mn{m_P|NGqVKKs|NF#mmS{9=j``M=MlY-DX0`7bBCCul%#joA0=m!!YH zzXo^k2UeCY>m$Mo&nJgReG(OUIMtb9(34iKK(1d{%J2cd4&tZpdi_?r!x=%(EyO zUB1?QZ^^ui=RPduBeZ&Z)yKuvMZW!})VMWzHzl@K#gh%Tv-X`^`X+B?hY$vAd0SBJ zDjJ59)(5;}{=8KCx`!#xV(MamyBh+hDa<(P%S$w5algd*M)Akx!VhKmcjPIpChbEm zC{joAn&w5!qYLIyZUleTEEeqBLuQc!B1D&VsrN1N)=ZkO+RqA<;ZTO=>Z# ztr_m%CJgFk(|m^d45c^uMZ3`-w0N}hIj=+OM57-^zL)wz?PJM7mySX0n=Cta*CtDA z?nFXc&u)J*wXgKKeacbLyqvv$>m8EOndEI-QY?p!iu-*hQ05h4jV*V8y$uq)-VlX0 zy@Py3TfFH@T6|jp>9=(F)VM7_N(*6wH%@s!8vQuSckZUcf9a>fTz=Nw0317Ni`g_t z)LLC@^cVdLcT?PD`#YWI;G1yHN%BeVtaA@i=$c*7H5+Yf?AXBUN254R<)HSurj6mB zHLy>~I%Op!He&xa{Gaf6)1bir5-$5a|Br>*|H>Oze6xpOVLcxadm1isvQS2j+%1Q{ z^*;LYxsQW!Be~q5eOUehB%8&($tWk?Xw~oJf-sR)DCC4SX}5ZH0k^jEiv2~6(n~|d zsu5b0Bgq68ZN3&y_Q?Be?l*>2CzWgu)z5{OhTK2lTN~}%CG9f#&q^I6YFj3hp3k}Z z%zL8C*S6#j|4gdUEgeWY;PM@!xkx7Q|f17X2{W+XnRVGd8a2w>gH| zM(35!ekE6~`c54goNmDS45OjEg&+o9Gve+G)Eb?&n7vKfefG<)Mmu>_n44yfX=Vg> zREz*?b3Uim89gGTA<`kaEtHLB$k-~&NSq+*$oPQRyK){bMYzS&t7y^%r>xepablaD zHNbw|`&aUd!U2##=A+((O>FyifA17)l&_iI{`@k7s9QK|ZR1yM=NINY7d`8;{N=!F zm$b}IU%fB5{#!{uKi$n6PcWyn;zc=dGpjX~cF!BXwsvJfG9Qo)S(}#+U|~8zkT*GN z@#G!nGB+a(7y`NUZ98xwmFL)!Sl_>YpUJ2`JPbAAcheZ7eoVU!vGolNVmM4+zea?I z+d{n!d=&PHj*d>9(>naZ;$m?FuR5BUnHf1X_4`X$f>l3Jnv=}zkDf|#yiF{HUpjom zesBaQgopQ`Lle6O_Tk|n>P;Ww5fWS2C0t^cPmtW2py=jt3&`@hk!@iD*#DE{uipJh zj>dc_9VuT`XHp@@I_MHMwe$AzD&WTCE}n0cWt-^2!wUtFa_J*A77_nT^=PPdg#W)- znKa&&wcH>Z8yhPZsaww1FrkwQSXSTtOk@O){UlK?q_B{_M6Y(pvXws4b1TCG69)&D zj*jkZd-Q2Cv%$AM`D|y+rOPg`cjSmcqkF4iWZ$8`GanXo+8iVhxY^D_L_rBAN7Vjd zB$~SSWw)s5C`zZ&JoT;=1btx}WA-2L(Q?aCO4q{$0z*T?<3E4IVo3#G|EyGUI$}KX z3G$1)4HmrY{>*7J@=b%$)7!hFyBqG;XUZ~xW+H&Ik_0UL6B+=)TO%sg~_bAP$axb9p?JmaJE`}c2h z0Rdu{%|THkBcu8W*-0hI1w05Q0fE(7t;yx#;&V47^`ZS{x*CV&n0P8_lldCEnOX-j zF)^``Vl9@12Dbs_P`^kYJ?I^7aW46Mj!+yN90pTZpWa>^boM9EvHIV6ejpPjCE~W{ z__Vr=Oi5XhDg!d?H-$8=waQ^3n=74y@rd^J_BClTKeL)U(^x^;i@oV#OFeL_?t(0T zT^vc3iM?|ZBctrAe!49Di-P?82)TUKJLoc2kPDj6tk=)bI8yhksTpej{aAYf0gC`8rc&HSTOnqw%%kA)s{9T9TWa+o}H}O)h5uhY#nJ%?tRNXoyOm{{?CJi&qCg~S_MTkF6C+oO45#%U6iqQJ~Q z9F~PDd-vyShYJNDXNQaP%0jfX@FyoHQf~zW1V(D?^rGBU&sDH4C2|htYde>h$G)}0$YKIfzb=@;dFm zV##rQaRJ{Z)ZP4@@-RYdUu~iBr}vxi-0)Jp+O>`llfk)Z8>he0JfF}RugGgg%|#N$6jN<7#S=4fY0x~FOntX zmp#7o^DkwgKCiQqx@3lWJ;ScY3|PzE*|^fo596mT*PBFLU0vBOTSI+=gD-6oih>e* z1yAN|$|!hvW{znnfG^ZU*3m&OUL3l`EaC0!>^Qo)g^1;dwEExwa9kBT+n>dT{Ij)Y z8jBE+5Gt0#v8Ygw@^W)WzQ|%v6saN1*E&c87cBnqRzf}FY-hapxJ5RB78>DV;^O5d z-N>&~UgaR`NjK^G5IUUx@}CbU8p;_|JN_H-dHkeO-)?r0+S8B zkyck%x96&DG`OR~!w*^0A2-U^&yONeE

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                            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 6e2985753d6..eb72db2dde1 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_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. + /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. From 2b0bf9a6ec08e7b0d756323fad70eaa35ff2326d Mon Sep 17 00:00:00 2001 From: MarkDownBot Date: Thu, 7 Dec 2023 15:00:15 +0000 Subject: [PATCH 32/32] 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 7419f7b4967..4f921fa78a8 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 0x55a06f621980 initialised + Tidegauge object at 0x562173632980 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 0x55a06f621980 initialised + Tidegauge object at 0x562173632980 initialised @@ -1100,7 +1100,7 @@ obs = coast.Tidegauge(dataset=tt) obs.dataset = obs.dataset.set_coords("time") ``` - Tidegauge object at 0x55a06f621980 initialised + Tidegauge object at 0x562173632980 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 0x55a06f621980 initialised - Tidegauge object at 0x55a06f621980 initialised - Tidegauge object at 0x55a06f621980 initialised + Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x562173632980 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 0x55a06f621980 initialised + Tidegauge object at 0x562173632980 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 0x55a06f621980 initialised + Tidegauge object at 0x562173632980 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 0x55a06f621980 initialised + Tidegauge object at 0x562173632980 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 0x55a06f621980 initialised - Tidegauge object at 0x55a06f621980 initialised + Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x562173632980 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 0x55a06f621980 initialised - Tidegauge object at 0x55a06f621980 initialised + Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x562173632980 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 0x55a06f621980 initialised + Tidegauge object at 0x562173632980 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 0x55a06f621980 initialised + Tidegauge object at 0x562173632980 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 0x55a06f621980 initialised - Tidegauge object at 0x55a06f621980 initialised + Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x562173632980 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 0x55a06f621980 initialised + Tidegauge object at 0x562173632980 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 0x55a06f621980 initialised - Tidegauge object at 0x55a06f621980 initialised + Tidegauge object at 0x562173632980 initialised + Tidegauge object at 0x562173632980 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 e6f83093485..64507a1787d 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 0x5560fa4f2980 initialised + Tidegauge object at 0x56422f0cb980 initialised Specify the data read as a High Low Water dataset. @@ -103,7 +103,7 @@ tg.dataset.plot.scatter(x="time", y="ssh") - +

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00:00:00 2001 From: MarkDownBot Date: Tue, 31 Oct 2023 13:18:42 +0000 Subject: [PATCH 04/32] Updated Altimetry notebook pages from coast repo. --- .../tidegauge_validation_tutorial.md | 180 +++++++++--------- .../Notebooks/Tidegauge/tidetable_tutorial.md | 14 +- .../tidegauge_validation_tutorial_16_1.png | Bin 58858 -> 58858 bytes .../tidegauge_validation_tutorial_18_0.png | Bin 61082 -> 61082 bytes .../tidegauge_validation_tutorial_20_0.png | Bin 51113 -> 51113 bytes .../tidegauge_validation_tutorial_22_1.png | Bin 66844 -> 66844 bytes .../tidegauge_validation_tutorial_24_1.png | Bin 50674 -> 50674 bytes .../tidegauge_validation_tutorial_35_0.png | Bin 63286 -> 63286 bytes .../tidegauge_validation_tutorial_44_0.png | Bin 74783 -> 74783 bytes .../tidegauge_validation_tutorial_57_0.png | Bin 96720 -> 96720 bytes .../tidegauge_validation_tutorial_59_0.png | Bin 76315 -> 76315 bytes .../tidegauge_validation_tutorial_63_1.png | Bin 24444 -> 24568 bytes .../tidegauge_validation_tutorial_65_1.png | Bin 29266 -> 29266 bytes .../tidetable_tutorial_14_1.png | Bin 12504 -> 12504 bytes 14 files changed, 99 insertions(+), 95 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 82b9f795901..56d3086c22c 100644 --- a/content/en/docs/Examples/Notebooks/Tidegauge/tidegauge_validation_tutorial.md +++ b/content/en/docs/Examples/Notebooks/Tidegauge/tidegauge_validation_tutorial.md @@ -64,7 +64,7 @@ We can create our empty tidegauge object: tidegauge = coast.Tidegauge() ``` - Tidegauge object at 0x561eabdbafc0 initialised + Tidegauge object at 0x56032c641fc0 initialised The `Tidegauge` class contains multiple methods for reading different typical @@ -461,7 +461,7 @@ 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