Added functions for trends and visualize climate

docs/files/table_linear_regression.csv

@@ -0,0 +1,14 @@
+index,LS_slope,LS_intercept,LS_r_square,TS_slope,TS_intercept,TS_slope_lower,TS_slope_upper,Kendall_tau,Kendal_p_value
+jan.,0.029711207446130317,-55.36514088996559,0.11816382666464985,0.025655241935483797,-47.54730342741921,-0.0126008064516129,0.07096774193548384,0.1948051948051948,0.21747455727039447
+feb.,-0.027866959956941727,57.608486263014086,0.11845730198747513,-0.028593749999999973,59.087466517857095,-0.06592261904761913,0.015277777777777748,-0.1948051948051948,0.21747455727039447
+mars,0.01768501484490265,-32.16031242600319,0.03870303537700306,0.024264705882352945,-45.23926707779887,-0.009879032258064477,0.055181451612903235,0.26406926406926406,0.09079907642795323
+april,0.0010476661020139201,0.005907914549234672,0.00024411362841211584,-0.002440476190476223,6.795535714285778,-0.030000000000000027,0.040722222222222194,-0.021645021645021644,0.9112884689584274
+mai,-0.009514398644833414,20.37348522795575,0.04579936634814474,-0.01908602150537633,39.273252688172015,-0.033064516129032384,0.006634897360703793,-0.2727272727272727,0.08028526277549888
+juni,-0.004398174289478636,10.135343967626573,0.014806729237167148,-0.003541666666666776,8.47114583333355,-0.02124999999999999,0.015708333333333324,-0.05627705627705628,0.7380940484479567
+juli,-0.005692974627055975,12.650315568022442,0.0337516274921531,-0.0015552995391705199,4.504349078341039,-0.021054147465437816,0.009646401985111668,-0.0303030303030303,0.8672669832921104
+aug.,0.0008877342853499994,-0.349576055080975,0.0008189992079955256,0.0018548387096774198,-2.2734274193548396,-0.013164136622390884,0.017679900744416884,0.0303030303030303,0.8672669832921104
+sep.,0.03726661020139281,-71.31799642386599,0.36449213098047584,0.04026041666666669,-77.17263020833337,0.013229166666666653,0.0629166666666667,0.4458874458874459,0.003207140388506757
+okt.,0.017707327735378236,-32.37476321014191,0.07780296692937472,0.014536290322580636,-26.135252016129012,-0.0141633064516129,0.054233870967742126,0.2121212121212121,0.17796763426213033
+nov.,0.02891821946169772,-54.03596955580651,0.20230887953096446,0.030370370370370332,-56.882199074074,0.0009444444444444736,0.06178030303030304,0.316017316017316,0.041288634586930426
+des.,0.010406003533633251,-17.15056692956409,0.020622058640743835,0.013709677419354804,-23.67379032258058,-0.021572580645161283,0.05241935483870974,0.16017316017316016,0.31397950542939346
+Year,0.008189232868174282,-13.846941088045558,0.2010254722688341,0.009867492576789694,-17.135340644259827,0.0012875215210719215,0.017266500622665003,0.35064935064935066,0.022775270000007175

docs/index.rst

@@ -1990,6 +1990,115 @@ Taylor diagram
 .. image:: files/Taylor_diagram.png
    :width: 500
 
+
+Climate and trends
+==================
+Yearly stripes
+--------------
+
+.. code-block:: python
+
+   plots.plot_yearly_stripes(
+      df,
+      var_name='HS',
+      method= 'mean',
+      ylabel='Hs [m]',
+      output_file='Yearly_stripes.png')
+
+.. image:: files/Yearly_stripes.png
+   :width: 500
+
+Monthly and yearly heatmap
+--------------------------
+
+.. code-block:: python
+
+   plots.plot_heatmap_monthly_yearly(
+      df,
+      var_name='T2m',
+      method= 'mean',
+      cb_label='2-m temperature [°C]',
+      output_file='Heatmap_monthly_yearly.png')
+
+.. image:: files/Heatmap_monthly_yearly.png
+   :width: 500
+
+Yearly vertical profiles
+------------------------
+
+.. code-block:: python
+
+   plots.plot_yearly_vertical_profiles(
+      df,
+      rad_colname='current_speed_',
+      method= 'mean',
+      yaxis_direction='down',
+      xlabel='Current speed [m/s]',
+      output_file='Yearly_vertical_profiles_current.png')
+
+.. image:: files/Yearly_vertical_profiles_current.png
+   :width: 500
+
+.. code-block:: python
+
+   plots.plot_yearly_vertical_profiles(
+      df,
+      rad_colname='W',
+      method= 'mean',
+      yaxis_direction='up',
+      xlabel='Wind speed [m/s]',
+      output_file='Yearly_vertical_profiles_wind.png')
+
+.. image:: files/Yearly_vertical_profiles_wind.png
+   :width: 500
+
+.. code-block:: python
+
+   plots.plot_heatmap_profiles_yearly(
+      df,
+      rad_colname='W',
+      cb_label='Wind speed [m/s]',
+      yaxis_direction='up',
+      method='P80',
+      output_file='Heatmap_yearly_profiles_wind.png')
+
+.. image:: files/Heatmap_yearly_profiles_wind.png
+   :width: 500
+
+Linear regression Plot and Table
+--------------------------------
+
+.. code-block:: python
+
+   plots.plot_yearly_vertical_profiles(
+      df,
+      var='HS',
+      time='Year',
+      stat='P70',
+      method=['Least-Squares','Theil-Sen'],
+      confidence_interval=0.95,
+      ylabel='Hs [m]',
+      output_figure='Linear_regression.png')
+
+.. image:: files/Linear_regression.png
+   :width: 500
+
+.. code-block:: python
+
+   tables.table_linear_regression(
+      df,
+      var='HS',
+      stat='mean',
+      method=['Least-Squares','Theil-Sen','Kendall-tau'],
+      confidence_interval=0.95,
+      intercept=True, 
+      output_file='table_linear_regression.csv'
+   )
+
+.. csv-table::
+   :header-rows: 1
+   :file: files/table_linear_regression.csv
+
 Auxiliary Functions
 ===================
 

examples/example_NORA_full.py

@@ -130,7 +130,7 @@
 # # fig = plots.plot_profile_monthly_stats(ds_ocean, var=['temp_' + d for d in depth], z=[float(d[:-1]) for d in depth], method='minimum',title='Min. Sea Temperature [°C]', output_file='plot_min_temp_profile_monthly_stats.png')
 # # fig = plots.plot_profile_monthly_stats(ds_ocean, var=['temp_' + d for d in depth], z=[float(d[:-1]) for d in depth], method='maximum',title='Max. Sea Temperature [°C]', output_file='plot_max_temp_profile_monthly_stats.png')
 
-# # # Sainity:
+# # # Salinity:
 # # df = tables.table_profile_monthly_stats(ds_ocean, var=['salt_' + d for d in depth], z=[float(d[:-1]) for d in depth], method='mean', output_file='table_mean_sal_profile_monthly_stats.png')
 # # df = tables.table_profile_monthly_stats(ds_ocean, var=['salt_' + d for d in depth], z=[float(d[:-1]) for d in depth], method='std.dev', output_file='table_std_sal_profile_monthly_stats.png')
 # # df = tables.table_profile_monthly_stats(ds_ocean, var=['salt_' + d for d in depth], z=[float(d[:-1]) for d in depth], method='minimum', output_file='table_min_sal_profile_monthly_stats.png')
@@ -150,3 +150,4 @@
 # #fig = plots.plot_storm_surge_for_given_hs(df,var_surge='zeta_0m', var_hs='HS', max_hs=20, output_file='surge_for_given_hs.png')
 # #df = tables.table_extreme_total_water_level(df, var_hs='HS',var_tp='TP',var_surge='zeta_0m', var_tide='tide', periods=[100,10000], output_file='table_extreme_total_water_level.csv')
 # #df = tables.table_storm_surge_for_rv_hs(df, var_hs='HS',var_tp='TP',var_surge='zeta_0m', var_tide='tide', periods=[1,10,100,10000],depth=200, output_file='table_storm_surge_for_rv_hs.csv')
+# #tide_type = stats.tidal_type(ds_tide,var='tide')

metocean_stats/plots/__init__.py

@@ -10,4 +10,5 @@
 from .extreme import *
 from .general import *
 from .verification import *
+from .climate import *
 from .spectra import *